System and method for automatic agricultural data collection

Abstract

The present invention relates to a system for automatic agricultural data collection, the system comprising at least a sensor module configured to acquire data related to at least an agricultural machine, at least an analyzing module configured to receive the data related to at least an agricultural machine and to determine at least an operational attribute of the at least one agricultural machine based on the data related to at least an agricultural machine, at least a processing module configured to generate at least a data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine based on the at least one operational attribute The present invention also relates to a corresponding method.

Description

[0001] System and method for automatic agricultural data collection

[0002] Field

[0003] The present invention lies in the field of agricultural data collection and, more particularly, in the field of automatic agricultural data collection. The present invention is directed to a system for agricultural data collection and to a method for agricultural data collection.

[0004] Background

[0005] Machines used in modern agricultural may include advanced control, computer and sensing systems that enable precise management and execution of various agricultural tasks, including the automatization of various agricultural task. During operation, advanced control, computer and sensing systems may generate and exchange a vast amount of data. Capturing, storing, and analyzing this data could greatly benefit farmers and agricultural businesses by, for example, optimizing resource use and improving product yields.

[0006] WO 2015 / 042540 Al discloses embodiments that provide a passive relay device for farming vehicles and implements, as well as an online farming data exchange, which together enable capturing, processing and sharing farming operation data generated during combined use of the farming vehicle and farming implement at a farming business. The farming operation data includes detailed information about individual farming operations, including without limitation the type of farming operation, the location of the farming operation, the travel path for the farming operation, as well as operating parameters and operating events occurring while the farming operation is performed.

[0007] US 11,096,323 B2 discloses a machine control system including an agricultural work machine having an ECU coupled via a system bus to control engine functions, a GPS receiver, data collector, and specialized guidance system including a stored program. The data collector captures agricultural geospatial data including location data for the work machine and data from the ECU, and executes the stored program to: (a) capture geometries of the farm; (b) capture agricultural geospatial data; (c) automatically classify the agricultural geospatial data using the geometries of the farm, into activity / event categories including operational, travel, and ancillary events; (d) aggregate the classified data to create geospatial data events; (e) match the geospatial data events to a model to generate matched events; (f) use the matched events to generate actionable information for the working machine in real time or near real-time; and (g) send operational directives to the agricultural work machine based on the actionable information.

[0008] US 10,109,024 B2 discloses a method. The method begins by a drive unit affiliated with farm equipment receiving data from the farm equipment to produce agricultural data. The method continues with the drive unit determining a filtering constraint based on one or more parameters selected from a plurality of lists of agricultural parameters and filtering the agricultural data based on the filtering constraint to produce filtered agricultural data. The method continues with the drive unit determining processing of the filtered agricultural data and executing the processing of the filtered agricultural data.

[0009] US 2024 / 0152786 Al discloses a method. The method may include receiving an indication that a current instance of an agricultural operation has begun in a geographic area; receiving task characteristics of the agricultural operation; generating a first task data structure including the task characteristics, a field identifier associated with the geographic area, and a task identifier; storing the first task data structure in a database as associated with a first time period; accessing a second task data structure for the agricultural operation associated with a second time period, the second time period being before the first time period; inputting the first and second task data structures into a difference model; receiving an output from the difference model identifying a difference for a first characteristic of the task characteristics between first task data structure and second task data structure; generating a user interface with the identified difference; and presenting the user interface on a computing device.

[0010] Existing technologies may rely on complex application programming interface (API) integrations to collect data. Such applications often show a high level of hardware complexity, which makes the applications rather specific and non-versatile. Besides the hardware complexity, such applications may also involve a large monetary investment and may therefore not be affordable.

[0011] Prior art technologies may further heavily rely on non-automatic data and procedures. For examples, advisory services used in this context are non-automatic and, besides, also involve a high cost. Further, current technologies are often dependent on data that is manually collected or reported, such as agricultural data reported by farmers or human viewing and analysis of satellite or camera images of agricultural fields.

[0012] There may be a need for a technology for data collection that does not rely on highly specialized and costly hardware, that does not rely on high initial investments, that is highly automatized, and / or that can identify features other than the time when and the place where an agricultural machine works.

[0013] Summary

[0014] The present invention alleviates, at least in part, the shortcomings of existing technologies.

[0015] In a first aspect, the present invention relates to system comprising at least a sensor module configured to acquire data related to at least an agricultural machine, at least an analyzing module configured to receive the data related to at least an agricultural machine and to determine at least an operational attribute of the at least one agricultural machine based on the data related to at least an agricultural machine, and at least a processing module configured to generate at least a data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine based on the at least one operational attribute.

[0016] The system may be a system for automatic agricultural data collection.

[0017] It will be understood that an agricultural machine may perform an agricultural activity. Data related to the at least on agricultural activity may be referred to as data related to the agricultural activity.

[0018] Embodiments of the present invention may be advantageous at least because of the reason of fully automatic data collection and the consequences thereof. Prior art technologies may rely, at least in part, on manually collected data. For instance, a large part of agricultural machines, e.g. tractors, used by farmers may not send task data related to at least an agricultural machine (e.g. task type, task location, task time) automatically. This may mean that a lot of data is collected manually by farmers, and often at the end of the farming season, which may be time-consuming and imprecise. Farmers may not want to spend time on data entry, especially during the busy farming season, so that often data entry work may be postponed to less busy seasons, e.g. winter, or may be neglected entirely, thus potentially leading to, e.g., dropping out from a carbon program farmers may be involved in. Farmers may often not remember what exact tasks were done and on which day on each field, especially if the farmer does data entry at the end of the farming season. It may be difficult for farmers to outsource this job to an advisor, since the advisor may not know enough of what happened on the farm and therefore a farmer may anyways have to spend time on communication. Some models may be run only once data is collected and therefore delay in data collection may result in delay in financial incentives, e.g. in carbon credit income. Further, if there are mistakes in the data collected, then farmers may lose subsidies, carbon credit income, sustainable loan benefits, and / or or some other valuable benefits.

[0019] The at least one agricultural machine may be, without limitation, a stationary agricultural machine and / or a movable agricultural machine and / or a tractor and / or a harvesting combine and / or a self-propelled sprayer and / or a self-driving sprayer.

[0020] The at least one agricultural machine may comprise a first agricultural machine and second agricultural machine, wherein the first agricultural machine may be configured to be different form the second agricultural machine, and wherein the first agricultural machine and the second agricultural machine may be configured to work, simultaneously, on the at least one agricultural zone.

[0021] The at least one agricultural machine may be in operation.

[0022] The sensor module may be configured to be installed on the at least one agricultural machine.

[0023] The system may comprise at least a communication module.

[0024] The system may comprise at least a server.

[0025] The system may comprise at least a storing module.

[0026] The communication module may be configured to be installed on the at least one agricultural machine.

[0027] The communication module may be connected to the internet.

[0028] The communication module may be configured to transmit the data related to at least an agricultural machine from the sensor module to the server.

[0029] The server may be configured to receive the data related to at least an agricultural machine.

[0030] The server may be configured to bidirectionally communicate with the storing module.

[0031] In other words, the data may be acquired by the sensor module, which may be installed on the at least one agricultural machine, and then transmitted to a server. The transmitting may be done with the communication module via the internet. For example, data may be GPS data. According to some embodiments, the sensor module can comprise a GPS tracker, comprising a GPS device. A GPS device may be installed to the tractor and be connected to the internet to send the data. The goal of the GPS tracker may be to send GPS location to the server where it may be saved, and eventually analyzed.

[0032] The storing module may be configured to store at least in part the data related to at least an agricultural machine.

[0033] The server may be configured to bidirectionally communicate with the analyzing module and / or with the processing module.

[0034] The storing module may be configured to store, at least in part, at least an output generated by the analyzing module and / or by the processing module.

[0035] The system may comprise at least an interface module.

[0036] The analyzing module may comprise an artificial intelligence module configured to execute at least an artificial intelligence algorithm.

[0037] The artificial intelligence module may be configured to be trained at least in part on the data related to at least an agricultural machine and / or on the at least one operational attribute.

[0038] The artificial intelligence module may be pre-trained.

[0039] The artificial intelligence module may be configured to be trained at least in part on data stored in the storing module.

[0040] The artificial intelligence module may be configured to be trained at least in part on historical data.

[0041] The artificial intelligence module may be configured to use at least one among: at least a supervised learning model, at least a unsupervised learning model, at least a reinforcement learning model, at least a generative module, at least a natural language processing model, at least a computer vision mode, at least a time series analysis model.

[0042] The at least one supervised learning model can comprise, for instance and without limitation, one or more among: Linear Regression, Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forest, Gradient Boosting Machines (GBM), XGBoost, Neural Networks, k-Nearest Neighbors (k-NN), Naive Bayes. The at least one unsupervised learning model can comprise, for instance and without limitation, one or more among: K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Autoencoders, Gaussian Mixture Models (GMM). The at least one reinforcement learning model can comprise, for instance and without limitation, one or more among: Q-Learning, Deep Q-Networks (DQN), Policy Gradients, Proximal Policy Optimization (PPO). The at least one generative model can comprise, for instance and without limitation, one or more among: Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs). The at least one natural language processing model can comprise, for instance and without limitation, one or more among: Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Transformer Models, BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pretrained Transformer). The at least one computer vision model can comprise, for instance and without limitation, one or more among: Convolutional Neural Networks (CNNs), YOLO (You Only Look Once), Faster R-CNN. The at least one time series analysis model can comprise, for instance and without limitation, one or more among: ARIMA (AutoReg ressive Integrated Moving Average), Seasonal Decomposition of Time Series (STL).

[0043] The interface module may be configured to utilize a plurality of software interfaces with different levels.

[0044] The interface module may be configured to provide, to an authorized user, access to any of the modules according to any of the embodiments of the precent invention.

[0045] The interface module may be configured to communicate with any combination of modules according to any of the embodiments of the present invention, said combination of modules comprising at least one module.

[0046] The interface module may be configured to change at least one parameter related to any combination of the modules according to any of the preceding system embodiments, said combination of modules comprising at least one module.

[0047] The artificial intelligence module may be configured to be trained at least in part based on at least an input of data to the artificial intelligence module from the interface module. The system may comprise at a modelling module.

[0048] The modelling module may be configured to receive at least an output from the analyzing module and / or the processing module. The modelling module may be configured to access data stored in the storing module.

[0049] The modelling module may be configured to generate a model based at least in part on an output from the analyzing module and / or the processing module.

[0050] The modelling module may be configured to generate a model based at least in part data stored in the storing module.

[0051] The system may be configured to arrange locally or remotely one or more among: the communication module, the analyzing module, the processing module, the server, the storing module, the interface module, the modelling module.

[0052] The system mya comprise at least a collective module, the collective module comprising the combination of any of the modules, according to any of the embodiments of the present invention, among: the communication module, the analyzing module, the processing module, the server, the storing module, the interface module.

[0053] The system may be configured to utilize different software for different purposes within a single module.

[0054] Any of the modules according to any of the embodiments of the present invention may comprise a computing device.

[0055] Embodiments of the present invention may be advantageous at least because of the integration with agricultural machines, such as tractors. Most recent tractors may have capability to collect data in the cloud where it can be sent to other software applications by using APIs. An implementation, according to existing technologies, may be connected (e.g. wired) to the tractor and this may give information about the task that is happening on the field. The problem may be that many tractors, and even most tractors in some regions, may not have capabilities to send information to the cloud. Further, API solutions may be expensive and highly specialized, and therefore hindering a widespread versatility to a variety of agricultural machines. This may mean that many farmers may have to do at least some level of manual data entry to the farm management systems and other reporting tools. Embodiments of the present invention may provide a simple, versatile, and easily-integrable data acquisition and processing solution. Additionally, even novel tractors may not get automatically information about, for example, crop type, cover crop type / existence, et cetera. This is where, for example, the utilization of an image recording device and / or at least a camera, according to embodiments of the present invention, can support also these tractors.

[0056] The data related to the at least one agricultural machine may comprise data related to at least a task of the at least one agricultural machine.

[0057] The sensor module may be configured to acquire data related to at least a task of the at least an agricultural machine, such that the task includes, without limitation, plowing and / or cultivating and / or disc cultivating and / or pi cultivating and / or harrowing and / or tilling and / or planting and / or fertilizing and / or spraying and / or harvesting and / or baling and / or mowing and / or loading and / or transporting and / or irrigating and / or raking and / or spreading manure and / or removing snow and / or regular drilling and / or direct drilling and / or liming.

[0058] The sensor module may be configured to acquire data utilizing at least a position sensor and / or at least a time sensor and / or a positioning system.

[0059] The sensor module may be configured to acquire data to acquire data utilizing at least a satellite navigation system.

[0060] The sensor module may be configured to acquire data utilizing at least a GPS navigation system.

[0061] It may be an advantage of embodiments of the present inventions to utilize at least a GPS navigation system. Contrary to at least some existing technologies, including e.g. the use of dedicated hardware and API, GPS-based solutions, and in particular GPS-only-based solutions, may be easy to implement to any tractor in any agricultural region, thus making the present technology accessible.

[0062] GPS-based solutions can give information about where an agricultural task happened and when it happened but it may not usually give information about what type of agricultural task happened on a field. It may be an advantage of the present invention to also provide information on the task type, as will be detailed in the following, and particularly to provide information on the task type based on GPS data.

[0063] There may be cases in existing technologies where a GPS tracker may be installed directly to an implement of an agricultural machine and this may help to detect what task is done since a specific task may done with a specific implement. However, this may be a problematic solution since implements may often not have power to give energy for the GPS tracker. Furthermore, there may be a multitude of implements used by a single tractor, which may lead to higher cost, since a specific GPS tracker may be needed for each implement. Embodiments of the present invention are at least partially directed to using a GPS-based solution in an economically efficient away. For example, a single GPS tracker may be used on a single agricultural machine, e.g. a tractor, and the GPS may be processed to determine the type of implement used by the agricultural machine or the type of task the agricultural machine executes.

[0064] The data related to the at least one agricultural machine may comprise data related to the location of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0065] The data related to the at least one agricultural machine may comprise data related to the time of work of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine. In other words, the data related to the at least one agricultural machine may comprise data related to when the at least one agricultural machine works.

[0066] The data related to the at least one agricultural machine comprises data related to the instant speed of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0067] The data related to the at least one agricultural machine comprises data related to a log of locations of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0068] The data related to the at least one agricultural machine comprises data related to a log of time of work of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0069] It will be understood that a work path of the at least one agricultural machine may refer to a specific route or track that the at least one agricultural machine follows while performing its designated tasks in the field. The work path may be derived, among other, from the log of locations of the at least one agricultural machine. The work path and / or the log of locations of the at least one agricultural machine, which can be based on GPS data, may be useful, in combination with, for example, knowledge of the widths that implements used by the at least one agricultural machine, in detecting, according to embodiments of the present invention, which implement is actually used by the at least one agricultural machine. The data related to the at least one agricultural machine comprises data related to the average speed of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0070] The data related to the at least one agricultural machine comprises data related to the work range of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0071] It will be understood that the work range of an agricultural machine may refer to the effective area or distance over which a machine or implement can perform its intended function within a given timeframe, usually in acres or hectares per day. The work range may depend, among others, on the implement that the agricultural machine uses (e.g. the width of the implement), as well and on the type of task. It may further depend on the type of machine, its capacity, and the nature of the task being performed. For example, tractors equipped with plows may cover 1-2 acres per hour, with the work range influenced by the plow width and soil type. Similarly, combine harvesters, which cut, thresh, and clean crops, can cover 20-50 acres per day, depending on their grain tank size and header width. Sprayers, essential for applying pesticides or fertilizers, can treat 100-200 acres daily, determined by the boom width and tank capacity. The work range of irrigation systems like center-pivot units can extend over hundreds of acres, often 130-160 acres per pivot. Thus, the work range may vary significantly based on, among others, machine type, attachment, and field conditions.

[0072] The data related to the at least one agricultural machine may comprise data related to the execution period of the work of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least an agricultural machine. The execution period may be a time or period of the year.

[0073] The data related to the at least one agricultural machine may comprise data related to the season of the year when the at least one agricultural machine works, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0074] The data related to the at least one agricultural machine may comprise data related to the location and / or to the time of work and / or to the average speed and / or to the work range and / or to the execution period of the work of the at least one agricultural machine acquired by using at least a satellite navigation system. The data related to the at least one agricultural machine may comprise data related to the location and / or to the time of work and / or to the average speed and / or to the work range and / or to the execution period of the work of the at least one agricultural machine acquired by using at least a position sensor and / or a time sensor.

[0075] The sensor module may be configured to acquire data utilizing at least a weather sensor and / or an internet connection.

[0076] The sensor module may be configured to acquire data from the internet via the communication module.

[0077] The data related to the at least one agricultural machine may comprise data related to the weather conditions when the at least one agricultural machine works, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0078] The data related to the at least one agricultural machine may comprise data related to the temperature when the at least one agricultural machine works, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0079] The data related to the at least one agricultural machine may comprise data related to the amount of rain in the period of time when the at least one agricultural machine works, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0080] The sensor module may be configured to acquire data utilizing at least an image recording device and / or at least a camera.

[0081] The data related to the at least one agricultural machine may comprise data related to at least one image of at least a part of the agricultural zone the at least one agricultural machine works on. It will be understood that the data related to at least one image may be interpreted as the at least on image.

[0082] Said at least one image may be an image of at least a crop in the agricultural zone.

[0083] Said at least one image may be an image of a field ground of the agricultural zone.

[0084] Said at least one image may be an image of an implement used by the at least one agricultural machine. For example, a camera installed in front or at the back of a tractor may take at least one image of the field the tractor is working on. The image, in particular, may be an image mainly concerning the crop the tractor works on. The camera may further be installed, for example, inside or outside of the tractor. The image may further be an image, for example, concerning the agricultural zone and / or the ground the tractor works on and / or concerning the implement that the tractor uses.

[0085] The data related to the at least one agricultural machine may comprise data related to the crop that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0086] The data related to the at least one agricultural machine comprises data related to at least a crop type that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0087] The data related to the at least one agricultural machine comprises data related to at least a cover crop and / or of a cover crop type and / or a plant and / or a biomass that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0088] The data related to the at least one agricultural machine comprises data related to at least a cover crop biomass that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0089] The data related to the at least one agricultural machine comprises data related to at least a crop yield of a crop that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0090] The sensor module may be configured to acquire data utilizing at least a Bluetooth device.

[0091] The sensor module may be configured to acquire data utilizing at least a Bluetooth device mounted on the at least one agricultural machine and at least a corresponding Bluetooth device mounted on at least an implement used by the at least one agricultural machine.

[0092] The sensor module may be configured to establish a communication between the at least one agricultural machine and at least one implement mounted on the at least one agricultural machine. The sensor module may be configured to establish a wireless communication between the at least one agricultural machine and at least one implement mounted on the at least one agricultural machine.

[0093] The sensor module may be configured to establish a wired communication between the at least one agricultural machine and at least one implement mounted on the at least one agricultural machine.

[0094] The sensor module may be configured establish a Bluetooth communication between at least a Bluetooth device mounted on the at least one agricultural machine and at least a Bluetooth device mounted on at least one implement used by the at least one agricultural machine.

[0095] The sensor module may be configured to acquire data related to at least an implement used by the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least an agricultural machine.

[0096] The data related to the at least one agricultural machine may comprise data related to the type of at least an implement used by the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least an agricultural machine.

[0097] The data related to the at least one agricultural machine may comprisesdata related to the dimension of at least an implement used by the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least an agricultural machine.

[0098] The at least one operational attribute may comprise a location of a task of the at least one agricultural machine. In other words, at least one operational attribute may relate to where the at least one agricultural machine works, i.e. to the location or place of the task of the at least one agricultural machine.

[0099] The analyzing module may be configured to determine the location of a task of the at least one agricultural machine based at least in part on data related to the location of the at least one agricultural machine.

[0100] The at least one operational attribute comprises a starting and / or ending and / or pausing time of a task of the at least one agricultural machine. In other words, at least one operational attribute may relate to when the at least one agricultural machine works, i.e. to the time of the task of the at least one agricultural machine.

[0101] The analyzing module may be configured to determine the starting and / or ending and / or pausing time of a task of the at least one agricultural machine based at least in part on data related to the time of work of the at least one agricultural machine.

[0102] The system may be configured to use an agricultural zone as the location of a task of the at least one agricultural machine.

[0103] The system may be configured to use an agricultural field as the location of a task of the at least one agricultural machine.

[0104] The location of a task of the at least one agricultural machine may comprise a position of the at least one agricultural machine, when the at least one agricultural machine executes the task.

[0105] The analyzing module may be configured to determine the position of the at least one agricultural machine based at least in part on data related to the location of the at least one agricultural machine.

[0106] The analyzing module may be configured to compare data related to the location of the at least one agricultural machine to reference coordinates, wherein said reference coordinates define the perimeter of an agricultural zone.

[0107] The analyzing module may be configured to identify if the at least one agricultural machine is located within the perimeter of the agricultural zone.

[0108] The analyzing module may be configured to identify the start of a task of the at least one agricultural machine when the at least one agricultural machine switches from being located out of the perimeter of the agricultural zone to being located within the perimeter of the agricultural zone.

[0109] The analyzing module may be configured to identify the end and / or the pause of a task of the at least one agricultural machine when the at least one agricultural machine switches from being located within the perimeter of the agricultural zone to being located out of the perimeter of the agricultural zone. The analyzing module may be configured to keep track of the position of the at least one agricultural machine from a first time to a second time based at least in part on data related to the location of the at least one agricultural machine and at least in part on data related to the time of work of the at least one agricultural machine.

[0110] The analyzing module may be configured to add a tolerance radius to the position of the at least one agricultural machine.

[0111] The tolerance radius may be set based on a detected implement.

[0112] The tolerance radius may be smaller or equal to 5 m, preferably smaller or equal to 10 m, more preferably smaller or equal to 30 m.

[0113] The analyzing module may be configured to identify the end of a task of the at least one agricultural machine, when the at least one agricultural machine switches from being located within the perimeter of the agricultural zone to being located out of the perimeter of the agricultural zone, and when the tracked position of the at least one agricultural machine covers more than a threshold portion of the agricultural zone.

[0114] The analyzing module may be configured to identify the start of a new task of the at least one agricultural machine after the end of a task of the at least one agricultural machine.

[0115] The analyzing module may be configured to identify a pause of a task of the at least one agricultural machine, when the at least one agricultural machine switches from being located within the perimeter of the agricultural zone to being located out of the perimeter of the agricultural zone, and when the tracked position of the at least one agricultural machine covers essentially less than threshold portion of the agricultural zone.

[0116] The analyzing module may be configured to delete the task and / or mark the task as ended and / or merge the task with another task of the at least one agricultural machine, after having identified the pause of said task.

[0117] The analyzing module may be configured to delete the task and / or mark the task as ended and / or merge the task with another task of the at least one agricultural machine, after having identified the pause said task, based at least in part on an input from the interface module. The analyzing module may be configured to set the threshold portion of the agricultural zone amount to 60% of the area of the agricultural zone, preferably to 80% of the area of the agricultural zone, more preferably to 95% of the area of the agricultural zone.

[0118] The artificial intelligence module may be configured to determine the threshold portion based at least in part on the training of the artificial intelligence.

[0119] The analyzing module may be configured to update the threshold portion.

[0120] The analyzing module may be configured to update the threshold portion based at least in part on an input coming from the interface module.

[0121] The analyzing module may be configured to determine an end of the task of the at least one agricultural machine after having identified the pause of said task, if the state of pause of said task persists for more than a threshold of time.

[0122] The analyzing module may be configured to determine an end of the task of the at least one agricultural machine on an agricultural zone after having identified the pause of said task, when said task is taken over by another agricultural machine on the same agricultural zone.

[0123] The analyzing module may be configured to identify a start of a continuation of the task of the at least one agricultural machine after having identified the pause of the task of the at least one agricultural machine, when the at least one agricultural machine switches from being located out of the perimeter of the agricultural zone to being located within the perimeter of the agricultural zone, wherein said switching happens after the analyzing module identifies the pause of the task of the at least one agricultural machine.

[0124] The analyzing module may be configured to determine an end of the task of the at least one agricultural machine after having identified the continuation of said task, when another task is started by the at least one agricultural machine on the agricultural zone.

[0125] The analyzing module may be configured to determine the start and / or pause and / or end of a task of the at least one agricultural machine based on an input to the analyzing module from the interface module.

[0126] The at least one operational attribute comprises at least a type of a task of the at least one agricultural machine. This may be advantageous. Generally, task data may often be collected manually by farmers or through tractor API integrations. GPS-solutions may be used to detect tractors' locations but not to detect what task tractors execute. Embodiments of the present invention are at least partially directed to determine a type of a task of the at least one agricultural machine, particularly based on GPS data. There may be existing GPS-based solutions that are able to detect task location (field where the task happened) and time (when the task happened), but, unlike embodiments of the present invention, may not detect task type based on this information. GPS may not have been used to automate task type collection. Embodiments of the present invention may at least in part be directed to automatic task type data collection and processing, preferably based essentially on GPS data.

[0127] The analyzing module may be configured to determine at least a type of a task of the at least one agricultural machine based at least in part on data related to the average speed of the at least one agricultural machine.

[0128] The analyzing module may be configured to determine at least a type of a task of the at least one agricultural machine based at least in part on data related to the location and / or to the time of work of the at least one agricultural machine.

[0129] The analyzing module may be configured to relate data related to the average speed of the at least one agricultural machine to at least an implement used by the at least one agricultural machine.

[0130] The analyzing module may be configured to determine at least a type of a task of the at least one agricultural machine based at least in part on data related to the work range of the at least one agricultural machine

[0131] The analyzing module may be configured to relate data related to the work range of the at least one agricultural machine to at least an implement used by the at least one agricultural machine.

[0132] The analyzing module may be configured to determine at least a type of a task of the at least one agricultural machine based at least in part on data related to the execution period of the work of the at least one agricultural machine.

[0133] The analyzing module may be configured to relate data related to the execution period of the work of the at least one agricultural machine to at least an implement used by the at least one agricultural machine. The analyzing module may be configured to determine at least a type of a task of the at least one agricultural machine based at least in part on data related to the weather conditions when the at least one agricultural machine works.

[0134] The analyzing module may be configured to relate data related to the weather conditions when the at least one agricultural machine works to at least an implement used by the at least one agricultural machine.

[0135] The analyzing module may be configured to determine at least a type of a task of the at least one agricultural machine based at least in part on the implement that the at least one agricultural machine uses.

[0136] The analyzing module may be configured to determine the at least one type of task of the at least one agricultural machine based on at least a logical relation between the average speed of the at least one agricultural machine and the at least one type of task of the at least one agricultural machine.

[0137] The analyzing module may be configured to assign a range of average speed of the at least one agricultural machine to the at least one type of task that the at least one agricultural machine executes at said average speed.

[0138] The analyzing module may be configured to compare the data related to the average speed of the at least one agricultural machine to the range of average speed assigned to the at least one type task of the at least one agricultural machine.

[0139] The analyzing module may be configured to determine the at least one of type task of the at least one agricultural machine if the data related to the average speed of the at least one agricultural machine is compatible with the range of average speed assigned to the at least one type task of the at least one agricultural machine.

[0140] The analyzing module may be configured to determine the at least one type of task of the at least one agricultural machine based on at least a logical relation between the work range of the at least one agricultural machine and the at least one type of task of the at least one agricultural machine.

[0141] The analyzing module may be configured to assign a span of work range of the at least one agricultural machine to the at least one type of task that the at least one agricultural machine executes at said average speed. The analyzing module may be configured to compare the data related to the work range of the at least one agricultural machine to the span of work range assigned to the at least one type task of the at least one agricultural machine.

[0142] The analyzing module may be configured to identify the at least one type of task of the at least one agricultural machine if the data related to the work range of the at least one agricultural machine is compatible with the span of work range assigned to the at least one type task of the at least one agricultural machine.

[0143] The analyzing module may be configured to identify the at least one type of task of the at least one agricultural machine based on at least a logical relation between the execution period of the work of the at least one agricultural machine and the at least one type of task of the at least one agricultural machine.

[0144] The analyzing module may be configured to assign a range of execution period of the work of the at least one agricultural machine to the at least one type of task that the at least one agricultural machine executes at said average speed.

[0145] The analyzing module may be configured to compare the data related to the execution period of the work of the at least one agricultural machine to the range of execution period of the work assigned to the at least one type task of the at least one agricultural machine.

[0146] The analyzing module may be configured to identify the at least one type of task of the at least one agricultural machine if the data related to the range of execution period of the at least one agricultural machine is compatible with range of execution period of the work assigned to the at least one type task of the at least one agricultural machine.

[0147] The analyzing module may be configured to identify the at least one type of task of the at least one agricultural machine based on at least a logical relation between the weather conditions when the at least one agricultural machine works and the at least one type of task of the at least one agricultural machine.

[0148] The analyzing module may be configured to assign a range of weather conditions of the at least one agricultural machine to the at least one type of task that the at least one agricultural machine executes at said average speed. The analyzing module may be configured to compare the data related to the weather conditions when the at least one agricultural machine works to the range of weather conditions assigned to the at least one type task of the at least one agricultural machine. The analyzing module may be configured to identify the at least one type of task of the at least one agricultural machine if the data related to the weather conditions when the at least one agricultural machine works is compatible with range weather conditions assigned to the at least one type task of the at least one agricultural machine.

[0149] The analyzing module may be configured to identify the at least one type of task of the at least one agricultural machine based on at least a logical relation between at least an implement that the at least one agricultural machine uses and at least a type of a task of the at least one agricultural machine.

[0150] The analyzing module may be configured to determine the ranges, according to embodiments of the present invention, based on an input to the analyzing module from the interface module.

[0151] The analyzing module may be configured to determine the at least one logical relation, according to embodiments of the present invention, based on an input to the analyzing module from the interface module.

[0152] The artificial intelligence module may be configured to determine the ranges, according to embodiments of the present invention, based on the training of the artificial intelligence.

[0153] The artificial intelligence module may be configured to determine the at least one logical relation, according to embodiments of the present invention, based on the training of the artificial intelligence.

[0154] The analyzing module may be configured to determine the at least one type of a task of the at least one agricultural machine based at least in part on data related to the image of at least a part of the agricultural zone the at least one agricultural machine works on.

[0155] The analyzing module may be configured to determine the at least one type of a task of the at least one agricultural machine based at least in part on data related to the image of at least a part of the agricultural zone the at least one agricultural machine works on, and / or based on the training of the artificial intelligence.

[0156] The artificial intelligence module may be configured to determine at least a crop type and / or cover crop and / or cover crop type and / or crop existence and / or cover crop existence and / or cover crop biomass and / or plant biomass and / or yield and / or an implement and / or a crop yield and / or the like based at least in part on data related to the image of at least a part of the agricultural zone the at least one agricultural machine works on, and / or based on the training of the artificial intelligence.

[0157] Embodiments of the present invention may exhibit the advantage of automated determination of parameters relating to an agricultural machine and its work, e.g. via an image and / or with the utilization of an artificial intelligence, and without human intervention.

[0158] The analyzing module may be configured to determine at least a type of a task of the at least one agricultural machine based at least in part on at least a type of implement used by the at least one agricultural machine, wherein the at least one type of implement is recognized by means of the Bluetooth connection between the at least one Bluetooth device mounted on the at least one agricultural machine and the at least one Bluetooth device mounted on at least one implement used by the at least one agricultural machine.

[0159] The at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine comprises a report on the utilization of an agricultural zone used by the at least one agricultural machine. In other words, the at least one data indicative of the utilization of at least an agricultural zone may be organized, for example as a report.

[0160] The report may be adapted for governmental reporting and / or for insurance reporting and / or for sustainable loan reporting and / or for soil organic carbon reporting and / or greenhouse gas emission reporting and / or carbon program reporting.

[0161] The report and / or the data related to the at least one agricultural machine can be adapted for training satellite-based verification solutions. In said solutions, satellite data can be used ed, for example, to verify which crops farmers apply, when farmers sow, when the harvest, if farmer cover crop and when, how often and when farmers cultivate, what type of cultivation farmers are busy with, and / or the like.

[0162] This may be particularly useful. It may in fact be known that governments may require reporting systems through an API. Governments may then check if a farmer followed all subsidy requirements. Analogously, sustainable loan reports may be required by, e.g., the European Union (EU), to detect if farmers follow requirements set by EU to be aligned with sustainable loan principles. Further, insurance providers may require reporting to check whether farmers apply certain practices. Moreover, many respected soil and greenhouse gas emission models may need a precise overview and / or report of tasks executed on the field to calculate soil organic carbon change or greenhouse gas emission. Stakeholders (e.g food companies, banks) may ask for greenhouse gas emission overviews and / or reports from their clients. Some farmers may join a carbon program where they get paid for additional carbon sequestration in the soil and / or greenhouse gas emission reductions compared to their previous practices. Such additional carbon sequestration in the soil and / or greenhouse gas emission reductions compared to their previous practices may need to be reported.

[0163] As an example, governments may develop their own reporting solutions where farmers may need to send data as a requirement to show that they comply with the rules that they may have agreed with while getting subsidies. Governments may also build APIs so that private companies can send data using the APIs. The data collection and processing according to embodiments of the present invention may be particularly suited and advantageous, for instance, for governmental reporting, or other reporting mentioned above. In particular, the data collection and processing according to embodiments of the present invention may be particularly easy and accessible compared to existing solutions because of, for instance, the use of mainly, if not only, GPS-data based solution for data acquisition and processing.

[0164] The data collected, according to embodiments of the present invention, for example the data related to the at least one agricultural machine and / or the data indicative of the utilization at least an agricultural zone, which might be combined with data from other sources, including what farmer may enter manually, may be analyzed, and compared with rules to alert a farmer if they do not follow certain requirements, e.g. governmental or insurance requirements.

[0165] The artificial intelligence module may be configured to analyze the at least one the at least one data indicative of the utilization of at least an agricultural zone according to a standardized database. In other words, embodiments of the present invention may be directed to using specific algorithms and Al to alert a farmer if he is going against rules.

[0166] For example, the data collected, according to embodiments of the present invention, for example the data related to the at least one agricultural machine and / or the data indicative of the utilization at least an agricultural zone, can be processed by the artificial intelligence module, based on a set of requirements, for example in written format, and the artificial intelligence module may generate automatically overview of mistakes the data may point to, as compared to the requirements. This may be advantageous, as it may allow to set up localized government reporting alerts in an efficient and quick way without developing specific systems for each country.

[0167] The report may comprise fields.

[0168] Said fields may relate at least in part to the location of a task of the at least one agricultural machine.

[0169] Said fields may relate at least in part to the starting and / or ending and / or pausing time of a task of the at least one agricultural machine.

[0170] Said fields may relate at least in part to the type of a task of the at least one agricultural machine.

[0171] The at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine may comprise an estimation of a change in greenhouse gas emission related at least in part to the at least one agricultural zone.

[0172] The at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine may comprise an estimation of a greenhouse gas emission related at least in part to the at least one agricultural zone.

[0173] The at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine may comprise an estimation of a greenhouse gas emission related at least in part to the at least one agricultural zone.

[0174] The at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine may comprise an estimation of a balance of soil organic carbon related at least in part to the at least one agricultural zone.

[0175] The at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine may comprise an estimation of a change of soil organic carbon related at least in part to the at least one agricultural zone.

[0176] The at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine may comprise an estimation of carbon sequestration related at least in part to the at least one agricultural zone. The estimations according to embodiments of the present inventions may be advantageous for reports and / or for models relating for instance to soil organic carbon and / or greenhouse gas emission and / or carbon programs and / or governments and / or insurances.

[0177] The system may be configured to generate a soil organic carbon model and / or a greenhouse gas emission model and / or a carbon sequestration model related to at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0178] The modelling module may be configured to generate a soil organic carbon model and / or a greenhouse gas emission model and / or a carbon sequestration model related to at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0179] The system may be configured to train at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine based on said model.

[0180] Overall, embodiments of the present invention may offer at least the advantage of the automated collection of precise data and of availability "for everyone", i.e of easy availability and accessibility. On the contrary, API-based solutions or advisory services may involve a high investment. Advisory services, as well as manual collection of data, may further involve problematic and incomplete data collection.

[0181] The system may be a system for optimizing the utilization of at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0182] The system may be a system for generating at least a report, said report concerning at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0183] The system may be a system for calculating soil organic carbon change, said change concerning at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0184] The system may be a system for calculating and / or modelling greenhouse gas emissions, said greenhouse gas emissions concerning at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine. The system may be a system for optimizing carbon sequestration, said carbon sequestration concerning at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0185] The system may be configured to train and / or optimize the at least one agricultural zone and / or the usage of said agricultural zone by the at least one agricultural machine based on the at least one operational attribute. In said training and / or optimization, the system, preferably via the interface module, may be configured to output at least a suggestion on the use of the at least one agricultural machine such as to reduce the difference between the at least one data indicative of the utilization of the at least one agricultural zone and at least a standardized value and / or a threshold value.

[0186] In some embodiments, the suggestion may relate to agroforestry. In this context, the system, preferably the Al module, may give a recommendation to plant agroforestry trees. The system may also detect trees and calculate their size. In some embodiments, the system, preferably the Al module, may give a recommendation relating to Soil Carbon, so as to ensure the increase of credits in a Soil Carbon program.

[0187] The interface module may be configured to receive from an authorized user said threshold value. The artificial intelligence module may be configured to determine said suggestion based at least in part on the training of the artificial intelligence module.

[0188] The interface module may be configured to receive user training data for the artificial intelligence module from an authorized user. The artificial intelligence module may be configured to be trained on said user training data, said user training data comprise at least an image of at least an implement that the at least one agricultural machine may use and the type of task of the at least one agricultural machine corresponding to said implement.

[0189] In an example, a farmer may upload a picture of each implement and tasks that are done with each implement. In a first exemplary scenario, said images together with context about related tasks may be used to train the Al module. In a second exemplary scenario, said images, together with context about related tasks, may be sent to the analyzing module, together with other, e.g. non-image, data coming from the sensor module to improve the detection accuracy of the Al module.

[0190] The artificial intelligence module may be configured to be trained on the data related to at least an agricultural machine, preferably image data of at least an implement that the at least one agricultural machine may use, and on said user training data, said user training data comprising the type of said implement and / or the type of task of the at least one agricultural machine corresponding to said implement.

[0191] In an example, the training of the Al module may be done, at least in part, starting from image data from the sensor module. In a first exemplary scenario, a user may just flag image data that represent a distinct implement (e.g. fertilizer drill, cultivator) and add which tasks are done with said implement, i.e. the task type. In a second exemplary scenario, the system, particularly the Al module, may automatically flag image data that represent a distinct implement (e.g. fertilizer drill, cultivator) and a user may add which tasks are done with said implement, i.e. the task type.

[0192] The artificial intelligence module may be configured to determine a type of task of the at least one agricultural machine and / or the type of implement used by the at least one agricultural machine before the at least one agricultural machine enters an agricultural field and / or an agricultural zone.

[0193] The artificial intelligence module may be configured to recognize and censor faces in the data acquired by the at least one image recording device and / or by the at least one camera. The artificial intelligence module may be configured to recognize faces in the image data acquired by the at least one image recording device and / or by the at least one camera and delete said data. The artificial intelligence module may be configured to recognize if the image data acquired by the at least one image recording device and / or by the at least one camera does not relate, at least in part, to an agricultural zone and / or to an agricultural field, and delete said data.

[0194] Simply put, the system may advantageously be privacy respecting. For example, the system may detect faces in image date and modify the image data image in a way that faces are not recognizable (e.g. via blurring). Additionally, or alternatively, the system may detect human faces in image data and delete said image date. In an example, the system may further delete image date that are taken outside of an agricultural field or zone.

[0195] The analyzing module, preferably the artificial intelligence module, may be configured to determine, based on data acquired by the at least one image recording device and / or by the at least one camera, the type of product that may be loaded on an implement that the at least one agricultural machine uses, when the product is being loaded and / or when the at least one agricultural machine uses the implement with the product.

[0196] In an example, the fertilizer that may be being used by the at least one agricultural machine may be determined. The analyzing module, preferably the artificial intelligence module, may be configured to send an intervention prompt to an authorized user, preferably via the interface module.

[0197] In the intervention prompt, the system, preferably via the interface module, may be configured to output at least a request of input to an authorized user, the input relating to the current and / or future use of an implement of the at least one agricultural machine.

[0198] In an example, the system, preferably the Al module, may notify an authorized person (e.g. tractor operator or farm manager) to ask extra information about a task of the at least one agricultural machine (e.g. application rate, fertilizer type, etc.). In another example, the system, preferably the Al module, may also notify an authorized person to provide an input to determine the type of task of the at least one agricultural machine based on image data that the Al module was not able to automatically determine. In another example, the system, preferably the Al module, may notify an authorized person once a farmer enters the agricultural field and / or zone. In another example, the system, preferably the Al module, may ask for an input to an authorized user whether they wish to using the same fertilizer for other tasks of the at least one agricultural machine (e.g. a tractor) other than the current task on the same day. In another example, whenever the Al module receives an input from an authorized user, e.g. via the interface module, the Al module may ask may send an alert (e.g. an SMS or mobile app notification) regarding data collected by the system to double-check the correctness of the input of the authorized user.

[0199] In the intervention prompt, the system, preferably via the interface module, may be configured to output at least a request of intervention on the sensor module to an authorized user.

[0200] In an example, the system, preferably the Al module, may automatically send a notification to an authorized user requesting that the imaging device and / or camera orientation be changed, for example be pointed further down or up. In another example, the Al module, may automatically send a notification to an authorized user requesting that the imaging device and / or camera orientation be checked, e.g. if the image data from the imaging device and / or camera orientation may be blurred and / or the like.

[0201] The at least on image recording device and / or the at least one camera may be secured to a transparent panel of the at least one agricultural machine. For example, the at least on image recording device and / or the at least one camera may be installed with a sticker on the inside or outside surface of a window of the at least one agricultural machine, e.g. a tractor. Additionally, or alternatively, the at least on image recording device and / or the at least one camera may be installed with a suction cup on the inside or outside surface of a window of the at least one agricultural machine, e.g. a tractor.

[0202] The at least on image recording device and / or the at least one camera may be powered via a battery present on the at least one agricultural machine. The at least on image recording device and / or the at least one camera may be powered via a 3-pin DIN connector. The at least on image recording device and / or the at least one camera may be powered via a power socket present in the cabin of the at least one agricultural machine.

[0203] In an example, the socket might be the cigarette lighter socket present on a tractor.

[0204] The analyzing module, preferably the Al module, may be configured to determine and / or update the at least one operational attribute at least when the at least one agricultural machine undergoes a task changing event. The artificial intelligence module may be configured to determine a type of task of the at least one agricultural machine and / or the type of implement used by the at least one agricultural machine when the at least one agricultural machine undergoes a task changing event. The task changing event may comprise the at least one of: the at least one agricultural machine leaving a task changing zone, the at least one agricultural machine coming to a halt, the at least one agricultural machine turning off, the at least one agricultural machine entering and / or leaving the agricultural zone and / or agricultural area.

[0205] In an example, the system may work based on task changing zones. The system may go into task detection mode (i.e. detect the task type of the at least one agricultural machine) at least one the at least one agricultural machine leaves task changing zone. In an example, the detection of the task type may be done via image data. In other words, the sensor module may take a picture, via e.g. the image recording device and / or the camera every time the agricultural machine leaves a task changing zone, which can for example be a warehouse of a farmer or the border of an agricultural field or zone. The localization of the agricultural machine with respect to the task changing zone may be done via the sensor module itself, via images of the surrounding or position data, e.g. GPS data. Generally, the system may go into task detection mode at least one the at least one agricultural machine undergoes a task changing event, which may also include, besides leaving a task changing zone, and as an example, the agricultural machine coming to a halt or being turned off for a threshold time interval. This may be set by an authorized user, for example via the interface module. Before determining the at least one type of task based on the data related to the image of at least a part of the agricultural zone, the analyzing module, preferably the Al module, may be configured to iteratively determine the quality the data related to the image and prompt the sensor module to acquire new data until the determined quality may be above a quality threshold.

[0206] In simple terms, for example, the system may acquire an image and analyze its quality. If the quality passes a quality test performed by the system, then the image may be used for determining, e.g. the task type of the agricultural machine. Otherwise, the system may take another picture and proceed iteratively.

[0207] The storing module may be, at least in part, remotely arranged and, at least in part, locally arranged on the at least one agricultural machine and the system may be configured to operate independently of the remotely arranged part of the storing module.

[0208] In an example, data may be stored locally until the agricultural machine reaches a position with connectivity allowing the bidirectional communication with remotely arranged modules, such as the remotely arranged part the storing module. Generally speaking, one or more embodiments of the present invention may be advantageous in terms of data consumption saving, such as 4G data consumption savings.

[0209] The system may be configured to acquire data utilizing at least one among the position sensor, the time sensor, the positioning system, the satellite navigation system, the GPS navigation system, the weather sensor, the internet connection at a frequency between 0.5 seconds and 5 minutes, preferably between 1 second and 1 minute, more preferably between 3 seconds and 7 seconds. The system may be configured to acquire data utilizing at least one among the image recording device, the camera at an acquisition frequency between 30 seconds and 15 minutes, preferably between 1 minute and 10 minutes, more preferably between 3 minutes and 7 minutes. The system, preferably via the interface module, may be configured to allow an authorized user to change said acquisition frequency(ies).

[0210] The system may be configured to acquire data utilizing at least one among the position sensor, the time sensor, the positioning system, the satellite navigation system, the GPS navigation system, the weather sensor, the internet connection upon a trigger signal. The system may be configured to acquire data utilizing at least one among the image recording device, the camera upon a trigger signal. The trigger signal may be received, preferably by the interface module, from an authorized user. The trigger signal may be remotely received, preferably by the interface module, from an authorized user. The trigger signal may be received when the at least one agricultural machine undergoes a task changing event. The trigger signal may relate to one or more events and / or conditions in the operation of the at least one agricultural machine.

[0211] Simply put, data acquisition may be related to certain events happening, in addition or alternatively with respect to regular frequency. In an example, data may be acquired every time the at least one agricultural machine enters an agricultural machine field and every day at 12:00. More complex acquisition schemes may be encompassed by embodiments of the present invention. Generally, data acquisition may be triggered remotely and / or may be triggered automatically based on conditions of the at least one agricultural machine.

[0212] In an example, GPS data may be acquired and / or sent between modules of the system every 5 seconds. Image data may be acquired and / or sent between modules of the system every 5 minutes. It might be possible, e.g. remotely from the sensor module, to change the acquisition frequencies: for example, it may be possible to set an acquisition frequency so that image data may be only taken every 1 minutes and / or so that image data may be only taken if a factor may be true (e.g. tractor entered the field). Similar consideration may apply for any type of sensor according to embodiments of the present invention.

[0213] The analyzing module, preferably the artificial intelligence module, may be configured to send an input prompt to an authorized user, preferably via the interface module. In the input prompt, the system, preferably via the interface module, may be configured to output at least a request of input to an authorized user, the input relating to the current and / or future use of an implement of the at least one agricultural machine and / or to the use of the at least one agricultural machine. The request of input may be sent upon a triggering event. The triggering event may relate to a change in the activity of the at least one agricultural machine. The input prompt may be a call carried out by the analyzing module, preferably by the Al module.

[0214] The Al module may be configured to run an Al agent. The call may be run by the Al agent. The system, preferably the analyzing module, more preferably the Al module, may be configured to store the input(s), preferably in the storing module. The Al module may be configured to by trained on the input(s) stored. The Al module may be configured to receive the data related to the at least one agricultural machine and build contextual awareness based thereon.

[0215] In exemplary embodiments, an automated phone call may be triggered by specific changes in the activity of the at least one agricultural machine (e.g., tractor entering a field, fertilization detected). The call may be used by the system to collect data about the task, field, or other relevant information from an authorized user. The callsmay use pre-recorded messages tailored to the detected activity (e.g., different prompts for fertilization vs. tillage). The call may be conducted by an Al agent with contextual awareness (e.g., tractor has entered a field, fertilizer details are needed). The Al agent may detect and collect relevant data during the call. The call may be recorded and stored. Recordings may be analyzed by the Al module to extract structured data. The Al module may use contextual knowledge of the farmer's typical product choices.

[0216] In an example, when an agricultural machine, such as a tractor, enters a field and a fertilization activity may be detected by the system, the system may trigger an automated phone call to a farmer. During this call, an Al agent may ask the farmer what fertilizer may be being used and at what application rate. Advantageously, embodiments of the present invention may compensate and / or improve, at least in part, the quality of reorganization of the task type by the system. In some case, the utilization of GPS and / or image data may need improvements. Furthermore, farmers may often forget key details if not promptly asked. Also, farmers may be accustomed to using phones while driving tractors.

[0217] The analyzing module, preferably the artificial intelligence module, may be configured to send an alert, preferably via the interface module, based on an alert threshold. The alert threshold may relate to a standardized use of the at least one agricultural machine and / or the agricultural field that the at least one agricultural machine works on. The interface module may be configured to receive from an authorized user said alert threshold. The Al module may be configured to determined said alert threshold.

[0218] For example, if the system detects that field activities conflict with predefined rules (e.g., government regulations), it may generate automated alerts. Alerts may be generated by the Al module with contextual awareness of the applicable rules.

[0219] The system may be configured to send the data related to at least an agricultural machine to an external Al model for training the external Al model.

[0220] In an example, data collected according to embodiments of the present invention may be used to improve and / or train models for verifying agricultural activities via satellite imagery.

[0221] In another aspect the present invention relates to a method comprising: acquiring data related to at least an agricultural machine, receiving the data related to at least an agricultural machine and determining at least an operational attribute of the at least one agricultural machine based on the data related to at least an agricultural machine, generating at least a data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine based on the at least one operational attribute.

[0222] The method may be a method for automatic agricultural data collection.

[0223] It will be understood that an agricultural machine may perform an agricultural activity. Data related to the at least on agricultural activity may be referred to as data related to the agricultural activity.

[0224] Embodiments of the present invention may be advantageous at least because of the reason of fully automatic data collection and the consequences thereof. Prior art technologies may rely, at least in part, on manually collected data. For instance, a large part of agricultural machines, e.g. tractors, used by farmers may not send task data related to at least an agricultural machine (e.g. task type, task location, task time) automatically. This may mean that a lot of data is collected manually by farmers, and often at the end of the farming season, which may be time-consuming and imprecise. Farmers may not want to spend time on data entry, especially during the busy farming season, so that often data entry work may be postponed to less busy seasons, e.g. winter, or may be neglected entirely, thus potentially leading to, e.g., dropping out from a carbon program farmers may be involved in. Farmers may often not remember what exact tasks were done and on which day on each field, especially if the farmer does data entry at the end of the farming season. It may be difficult for farmers to outsource this job to an advisor, since the advisor may not know enough of what happened on the farm and therefore a farmer may anyways have to spend time on communication. Some models may be run only once data is collected and therefore delay in data collection may result in delay in financial incentives, e.g. in carbon credit income. Further, if there are mistakes in the data collected, then farmers may lose subsidies, carbon credit income, sustainable loan benefits, and / or or some other valuable benefits.

[0225] The method may comprise using a stationary agricultural machine and / or a movable agricultural machine and / or a tractor and / or a harvesting combine and / or a self-propelled sprayer and / or a self-driving sprayer.

[0226] The method may comprise using at least a first agricultural machine and at least a second agricultural machine, wherein the at least one first agricultural machine may be configured to be different form the at least one second agricultural machine, and wherein the method may comprise using the at least one first agricultural machine and the at least on second agricultural machine simultaneously, on the at least one agricultural zone. The at least one agricultural machine may be in operation.

[0227] The method may comprise using at least a sensor module, wherein the method may comprise installing the at least one sensor module on the at least one agricultural machine.

[0228] The method may comprise using at least a communication module.

[0229] The method may comprise using at least a server.

[0230] The method may comprise using at least a storing module.

[0231] The method may comprise installing the communication module on the at least one agricultural machine.

[0232] The method may comprise connecting the communication module to the internet.

[0233] The method may comprise transmitting the data related to at least an agricultural machine from the sensor module to the server.

[0234] The method may comprise receiving data from the data related to at least an agricultural machine to the server.

[0235] The method may comprise bidirectionally communicating between the server and the storing module.

[0236] In other words, the data may be acquired by the sensor module, which may be installed on the at least one agricultural machine, and then transmitted to a server. The transmitting may be done with the communication module via the internet. For example, data may be GPS data. According to some embodiments, the sensor module can comprise a GPS tracker, comprising a GPS device. A GPS device may be installed to the tractor and be connected to the internet to send the data. The goal of the GPS tracker may be to send GPS location to the server where it may be saved, and eventually analyzed.

[0237] The method may comprise storing at least in part the data related to at least an agricultural machine in the storing module.

[0238] The method may comprise bidirectionally communicating between the server and the analyzing module and / or the processing module. The method may comprise storing in the storing module, at least in part, at least an output generated by the analyzing module and / or by the processing module.

[0239] The method may comprise using at least an interface module.

[0240] The method may comprise using an artificial intelligence module and executing at least an artificial intelligence algorithm in the artificial intelligence module.

[0241] The method may comprise training the artificial intelligence module at least in part on the data related to at least an agricultural machine and / or on the at least one operational attribute.

[0242] The method may comprises using a pre-trained artificial intelligence, particularly in the artificial intelligence module.

[0243] The method may comprise training the artificial intelligence module at least in part on data stored in the storing module.

[0244] The method may comprise training the artificial intelligence module at least in part on historical data.

[0245] The method may comprise using at least one among: at least a supervised learning model, at least a unsupervised learning model, at least a reinforcement learning model, at least a generative module, at least a natural language processing model, at least a computer vision mode, at least a time series analysis model.

[0246] The at least one supervised learning model can comprise, for instance and without limitation, one or more among: Linear Regression, Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forest, Gradient Boosting Machines (GBM), XGBoost, Neural Networks, k-Nearest Neighbors (k-NN), Naive Bayes. The at least one unsupervised learning model can comprise, for instance and without limitation, one or more among: K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Autoencoders, Gaussian Mixture Models (GMM). The at least one reinforcement learning model can comprise, for instance and without limitation, one or more among: Q-Learning, Deep Q-Networks (DQN), Policy Gradients, Proximal Policy Optimization (PPO). The at least one generative model can comprise, for instance and without limitation, one or more among: Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs). The at least one natural language processing model can comprise, for instance and without limitation, one or more among: Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Transformer Models, BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer). The at least one computer vision model can comprise, for instance and without limitation, one or more among: Convolutional Neural Networks (CNNs), YOLO (You Only Look Once), Faster R-CNN. The at least one time series analysis model can comprise, for instance and without limitation, one or more among: ARIMA (AutoRegressive Integrated Moving Average), Seasonal Decomposition of Time Series (STL).

[0247] The method may comprise utilizing a plurality of software interfaces with different levels.

[0248] The method may comprise providing, to an authorized user, access to any of the modules according to any embodiment of the present invention.

[0249] The method may comprise communicating with any combination of modules according to any of embodiment of the present invention, said combination of modules comprising at least one module.

[0250] The method may comprise changing at least one parameter related to any combination of the modules according to any embodiment of the present invention, said combination of modules comprising at least one module.

[0251] The method may comprise training the artificial intelligence module at least in part based on at least an input of data to the artificial intelligence module from the interface module.

[0252] The method may comprise using a modelling module.

[0253] The method may comprise receiving an output from an analyzing module and / or a processing module to the modelling module.

[0254] The method may comprise accessing data stored in the storing module.

[0255] The method may comprise generating a model based at least in part on an output from an analyzing module and / or a processing module.

[0256] The method may comprise generating a model based at least in part data stored in the storing module. The method may comprise arranging locally or remotely one or more among: the communication module, the analyzing module, the processing module, the server, the storing module, the interface module, the modelling module.

[0257] The method may comprise using at least a collective module, the collective module comprising the combination of any of the modules, according to any embodiment of the present invention, among: the communication module, the analyzing module, the processing module, the server, the storing module, the interface module.

[0258] The method may comprise utilizing different software for different purposes within a single module.

[0259] The method may comprise using a computing device, possibly in any of the modules according to embodiments of the present invention.

[0260] Embodiments of the present invention may be advantageous at least because of the integration with agricultural machines, such as tractors. Most recent tractors may have capability to collect data in the cloud where it can be sent to other software applications by using APIs. An implementation, according to existing technologies, may be connected (e.g. wired) to the tractor and this may give information about the task that is happening on the field. The problem may be that many tractors, and even most tractors in some regions, may not have capabilities to send information to the cloud. Further, API solutions may be expensive and highly specialized, and therefore hindering a widespread versatility to a variety of agricultural machines. This may mean that many farmers may have to do at least some level of manual data entry to the farm management systems and other reporting tools. Embodiments of the present invention may provide a simple, versatile, and easily-integrable data acquisition and processing solution. Additionally, even novel tractors may not get automatically information about, for example, crop type, cover crop type / existence, et cetera. This is where, for example, the utilization of an image recording device and / or at least a camera, according to embodiments of the present invention, can support also these tractors.

[0261] The data related to the at least one agricultural machine may comprise data related to at least a task of the at least one agricultural machine.

[0262] The method may comprise acquiring data related to at least a task of the at least an agricultural machine, such that the task includes plowing and / or cultivating and / or disc cultivating and / or pi cultivating and / or harrowing and / or tilling and / or planting and / or fertilizing and / or spraying and / or harvesting and / or baling and / or mowing and / or loading and / or transporting and / or irrigating and / or raking and / or spreading manure and / or removing snow and / or regular drilling and / or direct drilling and / or liming.

[0263] The method may comprise acquiring data utilizing at least a position sensor and / or at least a time sensor and / or at least a positioning system.

[0264] The method may comprise acquiring data utilizing at least a satellite navigation system.

[0265] The method may comprise acquiring data at least a GPS navigation system.

[0266] It may be an advantage of embodiments of the present inventions to utilize at least a GPS navigation system. Contrary to at least some existing technologies, including e.g. the use of dedicated hardware and API, GPS-based solutions, and in particular GPS-only-based solutions, may be easy to implement to any tractor in any agricultural region, thus making the present technology accessible.

[0267] GPS-based solutions can give information about where an agricultural task happened and when it happened but it may not usually give information about what type of agricultural task happened on a field. It may be an advantage of the present invention to also provide information on the task type, as will be detailed in the following, and particularly to provide information on the task type based on GPS data.

[0268] There may be cases in existing technologies where a GPS tracker may be installed directly to an implement of an agricultural machine and this may help to detect what task is done since a specific task may done with a specific implement. However, this may be a problematic solution since implements may often not have power to give energy for the GPS tracker. Furthermore, there may be a multitude of implements used by a single tractor, which may lead to higher cost, since a specific GPS tracker may be needed for each implement. Embodiments of the present invention are at least partially directed to using a GPS-based solution in an economically efficient away. For example, a single GPS tracker may be used on a single agricultural machine, e.g. a tractor, and the GPS may be processed to determine the type of implement used by the agricultural machine or the type of task the agricultural machine executes.

[0269] The data related to the at least one agricultural machine may comprise data related to the location of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine. The data related to the at least one agricultural machine may comprise data related to the time of work of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine. In other words, the data related to the at least one agricultural machine may comprise data related to when the at least one agricultural machine works.

[0270] The data related to the at least one agricultural machine may comprise data related to the instant speed of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0271] The data related to the at least one agricultural machine may comprise data related to a log of locations of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0272] The data related to the at least one agricultural machine may comprise data related to a log of time of work of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0273] It will be understood that a work path of the at least one agricultural machine may refer to a specific route or track that the at least one agricultural machine follows while performing its designated tasks in the field. The work path may be derived, among other, from the log of locations of the at least one agricultural machine. The work path and / or the log of locations of the at least one agricultural machine, which can be based on GPS data, may be useful, in combination with, for example, knowledge of the widths that implements used by the at least one agricultural machine, in detecting, according to embodiments of the present invention, which implement is actually used by the at least one agricultural machine.

[0274] The data related to the at least one agricultural machine may comprise data related to the average speed of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0275] The data related to the at least one agricultural machine may comprise data related to the work range of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0276] It will be understood that the work range of an agricultural machine may refer to the effective area or distance over which a machine or implement can perform its intended function within a given timeframe, usually in acres or hectares per day. The work range may depend, among others, on the implement that the agricultural machine uses (e.g. the width of the implement), as well and on the type of task. It may further depend on the type of machine, its capacity, and the nature of the task being performed. For example, tractors equipped with plows may cover 1-2 acres per hour, with the work range influenced by the plow width and soil type. Similarly, combine harvesters, which cut, thresh, and clean crops, can cover 20-50 acres per day, depending on their grain tank size and header width. Sprayers, essential for applying pesticides or fertilizers, can treat 100-200 acres daily, determined by the boom width and tank capacity. The work range of irrigation systems like center-pivot units can extend over hundreds of acres, often 130-160 acres per pivot. Thus, the work range may vary significantly based on, among others, machine type, attachment, and field conditions.

[0277] The data related to the at least one agricultural machine may comprise data related to the execution period of the work of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least an agricultural machine. The execution period may be a time or period of the year.

[0278] The data related to the at least one agricultural machine may comprise data related to the season of the year when the at least one agricultural machine works, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0279] The data related to the at least one agricultural machine may comprise data related to the location and / or to the time of work and / or to the average speed and / or to the work range and / or to the execution period of the work of the at least one agricultural machine acquired by using at least a satellite navigation system.

[0280] The data related to the at least one agricultural machine may comprise data related to the location and / or to the time of work and / or to the average speed and / or to the work range and / or to the execution period of the work of the at least one agricultural machine acquired by using at least a position sensor and / or a time sensor.

[0281] The method may comprise acquiring data utilizing at least a weather sensor.

[0282] The method may comprise acquiring data utilizing an internet connection.

[0283] The method may comprise acquiring data from the internet via the communication module. The data related to the at least one agricultural machine may comprise data related to the weather conditions when the at least one agricultural machine works, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0284] The data related to the at least one agricultural machine may comprise data related to the temperature when the at least one agricultural machine works, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0285] The data related to the at least one agricultural machine may comprise data related to the amount of rain in the period of time when the at least one agricultural machine works, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0286] The method may comprise acquiring data utilizing at least an image recording device.

[0287] The method may comprise acquiring data utilizing at least a camera.

[0288] The data related to the at least one agricultural machine may comprise data related to at least one image of at least a part of the agricultural zone the at least one agricultural machine works on.

[0289] Said at least one image may be an image of at least a crop in the agricultural zone.

[0290] Said at least one image may be an image of a field ground of the agricultural zone.

[0291] Said at least one image may be an image of an implement used by the at least one agricultural machine.

[0292] For example, a camera installed in front or at the back of a tractor may take at least one image of the field the tractor is working on. The image, in particular, may be an image mainly concerning the crop the tractor works on. The camera may further be installed, for example, inside or outside of the tractor. The image may further be an image, for example, concerning the agricultural zone and / or the ground the tractor works on and / or concerning the implement that the tractor uses.

[0293] The data related to the at least one agricultural machine may comprise data related to the crop that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine. The data related to the at least one agricultural machine may comprise data related to at least a crop type that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0294] The data related to the at least one agricultural machine may comprise data related to at least a cover crop and / or of a cover crop type and / or a plant and / or a biomass that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0295] The data related to the at least one agricultural machine may comprise data related to at least a cover crop biomass that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0296] The data related to the at least one agricultural machine may comprise data related to at least a crop yield of a crop that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0297] The method may comprise acquiring data utilizing at least a Bluetooth device.

[0298] The method may comprise acquiring data utilizing at least a Bluetooth device mounted on the at least one agricultural machine and at least a corresponding Bluetooth device mounted on at least an implement used by the at least one agricultural machine.

[0299] The method may comprise establishing a communication between the at least one agricultural machine and at least one implement mounted on the at least one agricultural machine.

[0300] The method may comprise establishing a wireless communication between the at least one agricultural machine and at least one implement mounted on the at least one agricultural machine.

[0301] The method may comprise establishing a wired communication between the at least one agricultural machine and at least one implement mounted on the at least one agricultural machine. The method may comprise establishing a Bluetooth communication between at least a Bluetooth device mounted on the at least one agricultural machine and at least a Bluetooth device mounted on at least one implement used by the at least one agricultural machine.

[0302] The method may comprise acquiring data related to at least an implement used by the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least an agricultural machine.

[0303] The data related to the at least one agricultural machine may comprise data related to the type of at least an implement used by the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least an agricultural machine.

[0304] The data related to the at least one agricultural machine may comprise data related to the dimension of at least an implement used by the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least an agricultural machine.

[0305] The at least one operational attribute may comprise a location of a task of the at least one agricultural machine. In other words, at least one operational attribute may relate to where the at least one agricultural machine works, i.e. to the location or place of the task of the at least one agricultural machine

[0306] The method may comprise determining the location of a task of the at least one agricultural machine based at least in part on data related to the location of the at least one agricultural machine.

[0307] The at least one operational attribute may comprise a starting and / or ending and / or pausing time of a task of the at least one agricultural machine. In other words, at least one operational attribute may relate to when the at least one agricultural machine works, i.e. to the time of the task of the at least one agricultural machine.

[0308] The method may comprise determining the starting and / or ending and / or pausing time of a task of the at least one agricultural machine based at least in part on data related to the time of work of the at least one agricultural machine.

[0309] The method may comprise using an agricultural zone as the location of a task of the at least one agricultural machine. The method may comprise using an agricultural field as the location of a task of the at least one agricultural machine.

[0310] The location of a task of the at least one agricultural machine comprises a position of the at least one agricultural machine, when the at least one agricultural machine executes the task.

[0311] The method may comprise determining the position of the at least one agricultural machine based at least in part on data related to the location of the at least one agricultural machine.

[0312] The method may comprise comparing data related to the location of the at least one agricultural machine to reference coordinates, wherein said reference coordinates define the perimeter of an agricultural zone.

[0313] The method may comprise identifying if the at least one agricultural machine is located within the perimeter of the agricultural zone.

[0314] The method may comprise identifying the start of a task of the at least one agricultural machine when the at least one agricultural machine switches from being located out of the perimeter of the agricultural zone to being located within the perimeter of the agricultural zone.

[0315] The method may comprise identifying the end and / or the pause of a task of the at least one agricultural machine when the at least one agricultural machine switches from being located within the perimeter of the agricultural zone to being located out of the perimeter of the agricultural zone.

[0316] The method may comprise keeping track of the position of the at least one agricultural machine from a first time to a second time based at least in part on data related to the location of the at least one agricultural machine and at least in part on data related to the time of work of the at least one agricultural machine.

[0317] The method may comprise adding a tolerance radius to the position of the at least one agricultural machine.

[0318] The method may comprise setting the tolerance radius based on a detected implement. The tolerance radius may be smaller or equal to 5 m, preferably smaller or equal to 10 m, more preferably smaller or equal to 30 m.

[0319] The method may comprise identifying the end of a task of the at least one agricultural machine, when the at least one agricultural machine switches from being located within the perimeter of the agricultural zone to being located out of the perimeter of the agricultural zone, and when the tracked position of the at least one agricultural machine covers more than a threshold portion of the agricultural zone.

[0320] The method may comprise identifying the start of a new task of the at least one agricultural machine after the end of a task of the at least one agricultural machine.

[0321] The method may comprise identifying a pause of a task of the at least one agricultural machine, when the at least one agricultural machine switches from being located within the perimeter of the agricultural zone to being located out of the perimeter of the agricultural zone, and when the tracked position of the at least one agricultural machine covers essentially less than threshold portion of the agricultural zone.

[0322] The method may comprise deleting the task and / or mark the task as ended and / or merge the task with another task of the at least one agricultural machine, after having identified the pause of said task.

[0323] The method may comprise deleting the task and / or marking the task as ended and / or merging the task with another task of the at least one agricultural machine, after having identified the pause said task, based at least in part on an input from the interface module.

[0324] The method may comprise setting the threshold portion of the agricultural zone amount to 60% of the area of the agricultural zone, preferably to 80% of the area of the agricultural zone, more preferably to 95% of the area of the agricultural zone.

[0325] The method may comprise using the artificial intelligence module to determine the threshold portion based at least in part on the training of the artificial intelligence.

[0326] The method may comprise updating the threshold portion.

[0327] The method may comprise updating the threshold portion based at least in part on an input coming from the interface module. The method may comprise determining an end of the task of the at least one agricultural machine after having identified the pause of said task, if the state of pause of said task persists for more than a threshold of time.

[0328] The method may comprise determining an end of the task of the at least one agricultural machine on an agricultural zone after having identified the pause of said task, when said task is taken over by another agricultural machine on the same agricultural zone.

[0329] The method may comprise identifying a start of a continuation of the task of the at least one agricultural machine after having identified the pause of the task of the at least one agricultural machine, when the at least one agricultural machine switches from being located out of the perimeter of the agricultural zone to being located within the perimeter of the agricultural zone, wherein said switching happens after the analyzing module identifies the pause of the task of the at least one agricultural machine.

[0330] The method may comprise determining an end of the task of the at least one agricultural machine after having identified the continuation of said task, when another task is started by the at least one agricultural machine on the agricultural zone.

[0331] The method may comprise determining the start and / or pause and / or end of a task of the at least one agricultural machine based on an input to the analyzing module from the interface module.

[0332] The at least one operational attribute may comprise at least a type of a task of the at least one agricultural machine.

[0333] This may be advantageous. Generally, task data may often be collected manually by farmers or through tractor API integrations. GPS-solutions may be used to detect tractors' locations but not to detect what task tractors execute. Embodiments of the present invention are at least partially directed to determine a type of a task of the at least one agricultural machine, particularly based on GPS data. There may be existing GPS-based solutions that are able to detect task location (field where the task happened) and time (when the task happened), but, unlike embodiments of the present invention, may not detect task type based on this information. GPS may not have been used to automate task type collection. Embodiments of the present invention may at least in part be directed to automatic task type data collection and processing, preferably based essentially on GPS data. The method may comprise determining at least a type of a task of the at least one agricultural machine based at least in part on data related to the average speed of the at least one agricultural machine.

[0334] The method may comprise determining at least a type of a task of the at least one agricultural machine based at least in part on data related to the location and / or to the time of work of the at least one agricultural machine.

[0335] The method may comprise relating data related to the average speed of the at least one agricultural machine to at least an implement used by the at least one agricultural machine.

[0336] The method may comprise determining at least a type of a task of the at least one agricultural machine based at least in part on data related to the work range of the at least one agricultural machine.

[0337] The method may comprise relating data related to the work range of the at least one agricultural machine to at least an implement used by the at least one agricultural machine.

[0338] The method may comprise determining at least a type of a task of the at least one agricultural machine based at least in part on data related to the execution period of the work of the at least one agricultural machine.

[0339] The method may comprise relating data related to the execution period of the work of the at least one agricultural machine to at least an implement used by the at least one agricultural machine.

[0340] The method may comprise determining at least a type of a task of the at least one agricultural machine based at least in part on data related to the weather conditions when the at least one agricultural machine works.

[0341] The method may comprise relating data related to the weather conditions when the at least one agricultural machine works to at least an implement used by the at least one agricultural machine.

[0342] The method may comprise determining at least a type of a task of the at least one agricultural machine based at least in part on the implement that the at least one agricultural machine uses. The method may comprise determining the at least one type of task of the at least one agricultural machine based on at least a logical relation between the average speed of the at least one agricultural machine and the at least one type of task of the at least one agricultural machine.

[0343] The method may comprise assigning a range of average speed of the at least one agricultural machine to the at least one type of task that the at least one agricultural machine executes at said average speed.

[0344] The method may comprise comparing the data related to the average speed of the at least one agricultural machine to the range of average speed assigned to the at least one type task of the at least one agricultural machine.

[0345] The method may comprise comparing the at least one of type task of the at least one agricultural machine if the data related to the average speed of the at least one agricultural machine is compatible with the range of average speed assigned to the at least one type task of the at least one agricultural machine.

[0346] The method may comprise determining the at least one type of task of the at least one agricultural machine based on at least a logical relation between the work range of the at least one agricultural machine and the at least one type of task of the at least one agricultural machine.

[0347] The method may comprise assigning a span of work range of the at least one agricultural machine to the at least one type of task that the at least one agricultural machine executes at said average speed.

[0348] The method may comprise comparing the data related to the work range of the at least one agricultural machine to the span of work range assigned to the at least one type task of the at least one agricultural machine.

[0349] The method may comprise identifying the at least one type of task of the at least one agricultural machine if the data related to the work range of the at least one agricultural machine is compatible with the span of work range assigned to the at least one type task of the at least one agricultural machine.

[0350] The method may comprise identifying the at least one type of task of the at least one agricultural machine based on at least a logical relation between the execution period of the work of the at least one agricultural machine and the at least one type of task of the at least one agricultural machine.

[0351] The method may comprise assigning a range of execution period of the work of the at least one agricultural machine to the at least one type of task that the at least one agricultural machine executes at said average speed.

[0352] The method may comprise comparing the data related to the execution period of the work of the at least one agricultural machine to the range of execution period of the work assigned to the at least one type task of the at least one agricultural machine.

[0353] The method may comprise identifying the at least one type of task of the at least one agricultural machine if the data related to the range of execution period of the at least one agricultural machine is compatible with range of execution period of the work assigned to the at least one type task of the at least one agricultural machine.

[0354] The method may comprise the at least one type of task of the at least one agricultural machine based on at least a logical relation between the weather conditions when the at least one agricultural machine works and the at least one type of task of the at least one agricultural machine.

[0355] The method may comprise assigning a range of weather conditions of the at least one agricultural machine to the at least one type of task that the at least one agricultural machine executes at said average speed.

[0356] The method may comprise comparing the data related to the weather conditions when the at least one agricultural machine works to the range of weather conditions assigned to the at least one type task of the at least one agricultural machine.

[0357] The method may comprise identifying the at least one type of task of the at least one agricultural machine if the data related to the weather conditions when the at least one agricultural machine works is compatible with range weather conditions assigned to the at least one type task of the at least one agricultural machine.

[0358] The method may comprise identifying the at least one type of task of the at least one agricultural machine based on at least a logical relation between at least an implement that the at least one agricultural machine uses and at least a type of a task of the at least one agricultural machine. The method may comprise determining the ranges, according to embodiments of the present invention, based on an input from the interface module.

[0359] The method may comprise determining the at least one logical relation, according to embodiments of the present invention, based on an input from the interface module.

[0360] The method may comprise determining, preferably with the artificial intelligence module, the ranges, according to embodiments of the present invention, based on the training of the artificial intelligence.

[0361] The method may comprise determining, preferably with the artificial intelligence module, the at least one logical relation, according to embodiments of the present invention, based on the training of the artificial intelligence.

[0362] The method may comprise determining the at least one type of a task of the at least one agricultural machine based at least in part on data related to the image of at least a part of the agricultural zone the at least one agricultural machine works on.

[0363] The method may comprise determining, preferably with the artificial intelligence module, the at least one type of a task of the at least one agricultural machine based at least in part on data related to the image of at least a part of the agricultural zone the at least one agricultural machine works on, and / or based on the training of the artificial intelligence.

[0364] The method may comprise determining, preferably with the artificial intelligence module, at least a crop type and / or cover crop and / or cover crop type and / or crop existence and / or cover crop existence and / or cover crop biomass and / or plant biomass and / or yield and / or an implement and / or a crop yield and / or the like based at least in part on data related to the image of at least a part of the agricultural zone the at least one agricultural machine works on, and / or based on the training of the artificial intelligence.

[0365] Embodiments of the present invention may exhibit the advantage of automated determination of parameters relating to an agricultural machine and its work, e.g. via an image and / or with the utilization of an artificial intelligence, and without human intervention.

[0366] The method may comprise determining at least a type of a task of the at least one agricultural machine based at least in part on at least a type of implement used by the at least one agricultural machine, wherein the at least one type of implement is recognized by means of the Bluetooth connection between the at least one Bluetooth device mounted on the at least one agricultural machine and the at least one Bluetooth device mounted on at least one implement used by the at least one agricultural machine.

[0367] The at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine may comprise a report on the utilization of an agricultural zone used by the at least one agricultural machine.

[0368] The report may be adapted for governmental reporting and / or for insurance reporting and / or for sustainable loan reporting and / or for soil organic carbon reporting and / or greenhouse gas emission reporting and / or carbon program reporting.

[0369] The method may comprise, inter alia, reporting to governments and / or to insurances.

[0370] The report and / or the data related to the at least one agricultural machine can be adapted for training satellite-based verification solutions. In said solutions, satellite data can be used ed, for example, to verify which crops farmers apply, when farmers sow, when the harvest, if farmer cover crop and when, how often and when farmers cultivate, what type of cultivation farmers are busy with, and / or the like.

[0371] This may be particularly useful. It may in fact be known that governments may require reporting systems through an API. Governments may then check if a farmer followed all subsidy requirements. Analogously, sustainable loan reports may be required by, e.g., the European Union (EU), to detect if farmers follow requirements set by EU to be aligned with sustainable loan principles. Further, insurance providers may require reporting to check whether farmers apply certain practices. Moreover, many respected soil and greenhouse gas emission models may need a precise overview and / or report of tasks executed on the field to calculate soil organic carbon change or greenhouse gas emission. Stakeholders (e.g food companies, banks) may ask for greenhouse gas emission overviews and / or reports from their clients. Some farmers may join a carbon program where they get paid for additional carbon sequestration in the soil and / or greenhouse gas emission reductions compared to their previous practices. Such additional carbon sequestration in the soil and / or greenhouse gas emission reductions compared to their previous practices may need to be reported.

[0372] As an example, governments may develop their own reporting solutions where farmers may need to send data as a requirement to show that they comply with the rules that they may have agreed with while getting subsidies. Governments may also build APIs so that private companies can send data using the APIs. The data collection and processing according to embodiments of the present invention may be particularly suited and advantageous, for instance, for governmental reporting, or other reporting mentioned above. In particular, the data collection and processing according to embodiments of the present invention may be particularly easy and accessible compared to existing solutions because of, for instance, the use of mainly, if not only, GPS-data based solution for data acquisition and processing.

[0373] The data collected, according to embodiments of the present invention, for example the data related to the at least one agricultural machine and / or the data indicative of the utilization at least an agricultural zone, which might be combined with data from other sources, including what farmer may enter manually, may be analyzed, and compared with rules to alert a farmer if they do not follow certain requirements, e.g. governmental or insurance requirements.

[0374] The method may comprise analyzing, preferably with the artificial intelligence module, the at least one the at least one data indicative of the utilization of at least an agricultural zone according to a standardized database. In other words, embodiments of the present invention may be directed to using specific algorithms and Al to alert a farmer if he is going against rules.

[0375] For example, the data collected, according to embodiments of the present invention, for example the data related to the at least one agricultural machine and / or the data indicative of the utilization at least an agricultural zone, can be processed by the artificial intelligence module, based on a set of requirements, for example in written format, and the artificial intelligence module may generate automatically overview of mistakes the data may point to, as compared to the requirements. This may be advantageous, as it may allow to set up localized government reporting alerts in an efficient and quick way without developing specific systems for each country.

[0376] The report may comprise fields.

[0377] Said fields may relate at least in part to the location of a task of the at least one agricultural machine.

[0378] Said fields may relate at least in part to the starting and / or ending and / or pausing time of a task of the at least one agricultural machine.

[0379] Said fields may relate at least in part to the type of a task of the at least one agricultural machine. The at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine may comprise an estimation of a change in greenhouse gas emission related at least in part to the at least one agricultural zone.

[0380] The at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine may comprise an estimation of a greenhouse gas emission related at least in part to the at least one agricultural zone.

[0381] The at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine may comprise an estimation of a greenhouse gas emission related at least in part to the at least one agricultural zone.

[0382] The at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine may comprise an estimation of a balance of soil organic carbon related at least in part to the at least one agricultural zone.

[0383] The at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine may comprise an estimation of a change of soil organic carbon related at least in part to the at least one agricultural zone.

[0384] The at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine may comprise an estimation of carbon sequestration related at least in part to the at least one agricultural zone.

[0385] The estimations according to embodiments of the present inventions may be advantageous for reports and / or for models relating for instance to soil organic carbon and / or greenhouse gas emission and / or carbon programs and / or governments and / or insurances.

[0386] The method may comprise generating a soil organic carbon model and / or a greenhouse gas emission model and / or a carbon sequestration model related to at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0387] The method may comprise generating, preferably with the modelling module, a soil organic carbon model and / or a greenhouse gas emission model and / or a carbon sequestration model related to at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0388] The method may comprise comprises training at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine based on said model. Overall, embodiments of the present invention may offer at least the advantage of the automated collection of precise data and of availability "for everyone", i.e of easy availability and accessibility. On the contrary, API-based solutions or advisory services may involve a high investment. Advisory services, as well as manual collection of data, may further involve problematic and incomplete data collection.

[0389] The method may be a method for optimizing the utilization of at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0390] The method may be a method for generating at least a report, said report concerning at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0391] The method may be a method for calculating soil organic carbon change, said change concerning at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0392] The method may be a method for calculating and / or modelling greenhouse gas emissions, said greenhouse gas emissions concerning at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0393] The method may be a method for optimizing carbon sequestration, said carbon sequestration concerning at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0394] The method may comprise training and / or optimizing the at least one agricultural zone and / or the usage of said agricultural zone by the at least one agricultural machine based on the at least one operational attribute. In said training and / or optimization, the method may comprise outputting at least a suggestion on the use of the at least one agricultural machine such as to reduce the difference between the at least one data indicative of the utilization of the at least one agricultural zone and at least a standardized value and / or a threshold value. The method may comprise receiving from an authorized user said threshold value. The method may comprise determining said suggestion based at least in part on the training of the artificial intelligence module.

[0395] The method may comprise receiving user training data for the artificial intelligence module from an authorized user. The method may comprise training the artificial intelligence module on said user training data said, said data comprising at least an image of at least an implement that the at least one agricultural machine may use and the type of task of the at least one agricultural machine corresponding to said implement. The method may comprise training the artificial intelligence module on the data related to at least an agricultural machine, preferably image data of at least an implement that the at least one agricultural machine may use, and on said user training data, said user training data comprising the type of said implement and / or the type of task of the at least one agricultural machine corresponding to said implement.

[0396] The method may comprise determining a type of task of the at least one agricultural machine and / or the type of implement used by the at least one agricultural machine before the at least one agricultural machine enters an agricultural field and / or an agricultural zone.

[0397] The method may comprise recognizing and censoring faces in the data acquired by the at least one image recording device and / or by the at least one camera. The method may comprise recognizing faces in the image data acquired by the at least one image recording device and / or by the at least one camera and delete said data. The method may comprise recognizing if the image data acquired by the at least one image recording device and / or by the at least one camera does not relate, at least in part, to an agricultural zone and / or to an agricultural field, and delete said data.

[0398] The method may comprise determining, based on data acquired by the at least one image recording device and / or by the at least one camera, the type of product that is loaded on an implement that the at least one agricultural machine uses, when the product is being loaded and / or when the at least one agricultural machine uses the implement with the product.

[0399] The method may comprise sending an intervention prompt to an authorized user, preferably via the interface module. In the intervention prompt, the method may comprise outputting at least a request of input to an authorized user, the input relating to the current and / or future use of an implement of the at least one agricultural machine. In the intervention prompt, the method may comprise outputting at least a request of intervention on the sensor module to an authorized user.

[0400] The at least on image recording device and / or the at least one camera is secured to a transparent panel of the at least one agricultural machine. The at least on image recording device and / or the at least one camera may be powered via a battery present on the at least one agricultural machine. The at least on image recording device and / or the at least one camera may be powered via a 3-pin DIN connector. The at least on image recording device and / or the at least one camera may be powered via a power socket present in the cabin of the at least one agricultural machine.

[0401] The method may comprise determining and / or updating the at least one operational attribute at least when the at least one agricultural machine undergoes a task changing event. The method may comprise determining a type of task of the at least one agricultural machine and / or the type of implement used by the at least one agricultural machine when the at least one agricultural machine undergoes a task changing event. The task changing event may comprise the at least one of: the at least one agricultural machine leaving a task changing zone, the at least one agricultural machine coming to a halt, the at least one agricultural machine turning off, the at least one agricultural machine entering and / or leaving the agricultural zone and / or agricultural area.

[0402] Before determining the at least one type of task based on the data related to the image of at least a part of the agricultural zone, the method may comprise iteratively determining the quality the data related to the image and prompt the sensor module to acquire new data until the determined quality is above a quality threshold.

[0403] The storing module may be, at least in part, remotely arranged and, at least in part, locally arranged on the at least one agricultural machine and wherein the system is configured to operate independently of the remotely arranged part of the storing module.

[0404] The method may comprise acquiring data utilizing at least one among the position sensor, the time sensor, the positioning system, the satellite navigation system, the GPS navigation system, the weather sensor, the internet connection at a frequency between 0.5 seconds and 5 minutes, preferably between 1 second and 1 minute, more preferably between 3 seconds and 7 seconds. The method may comprise acquiring data utilizing at least one among the image recording device, the camera at an acquisition frequency between 30 seconds and 15 minutes, preferably between 1 minute and 10 minutes, more preferably between 3 minutes and 7 minutes. The method may comprise allowing an authorized user to change said acquisition frequency(ies).

[0405] The method may comprise acquiring data utilizing at least one among the position sensor, the time sensor, the positioning system, the satellite navigation system, the GPS navigation system, the weather sensor, the internet connection upon a trigger signal. The method may comprise acquiring data utilizing at least one among the image recording device, the camera upon a trigger signal. The trigger signal may be received, preferably by the interface module, from an authorized user. The trigger signal may be remotely received, preferably by the interface module, from an authorized user. The trigger signal may be received when the at least one agricultural machine undergoes a task changing event. The trigger signal may relate to one or more events and / or conditions in the operation of the at least one agricultural machine.

[0406] The method may comprise sending an input prompt to an authorized user, preferably via the interface module. In the input prompt, the method may comprise outputting at least a request of input to an authorized user, the input relating to the current and / or future use of an implement of the at least one agricultural machine and / or to the use of the at least one agricultural machine. The request of input may be sent upon a triggering event. The triggering event may relate to a change in the activity of the at least one agricultural machine. The input prompt may be a call carried out by the analyzing module, preferably by the Al module.

[0407] The method may comprise running an Al agent. The call may be run by the Al agent. The method may comprise storing the input(s), preferably in the storing module. The method may comprise training the Al module on the input(s) stored. The method may comprise providing the Al module with the data related to the at least one agricultural machine and the Al module building contextual awareness based thereon.

[0408] The method may comprise sending an alert based on an alert threshold. The alert threshold may relate to a standardized use of the at least one agricultural machine and / or the agricultural field that the at least one agricultural machine works on. The method may comprise receiving from an authorized user said alert threshold. The method may comprise determining with the Al module said alert threshold.

[0409] The method may comprise sending the data related to at least an agricultural machine to an external Al model for training the external Al model.

[0410] The method may comprise carrying out the method according to any embodiments of the present invention via the system according any embodiments of the present invention

[0411] The system may be adapted to carry out the method recited in any embodiment of the present invention.

[0412] The system may be adapted to carry out any given step of the method recited in any embodiment of the present invention.

[0413] In another aspect, the present invention relates to a use of the system according to embodiments of the present invention. The use may be for carrying out the method according to embodiments of the present invention.

[0414] In another aspect, the present invention relates to a computer program comprising instructions which, when executed by a processing component, cause the component to carry out the method according embodiments of the present invention.

[0415] In one aspect, the present invention relates to data carried signal carrying the computer program.

[0416] In another aspect, the present invention relates to a computer program product comprising instructions which, when executed by a processing component, cause the component to carry out the method according embodiments of the present invention.

[0417] In another aspect, the present invention relates to a data carried signal carrying the computer program product.

[0418] In another aspect, the present invention relates to a computer-readable medium comprising instructions which, when executed by a processor, cause the computer to carry out the method according embodiments of the present invention.

[0419] The present technology is also described by the following numbered embodiments.

[0420] Below, system embodiments are presented. System embodiments are abbreviated by the letter "S" followed by a number. Whenever reference is made herein to "system embodiments", these embodiments are meant.

[0421] SI. A system comprising:

[0422] at least a sensor module configured to acquire data related to at least an agricultural machine,

[0423] at least an analyzing module configured to receive the data related to at least an agricultural machine and to determine at least an operational attribute of the at least one agricultural machine based on the data related to at least an agricultural machine,

[0424] at least a processing module configured to generate at least a data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine based on the at least one operational attribute. 52. The system according to the preceding embodiment, wherein the system is a system for automatic agricultural data collection.

[0425] 53. The system according to any of the preceding embodiments, wherein the at least one agricultural machine is a stationary agricultural machine.

[0426] 54. The system according to any of the preceding embodiments, wherein the at least one agricultural machine is a movable agricultural machine.

[0427] 55. The system according to any of the preceding embodiments, wherein the at least one agricultural machine is a tractor.

[0428] 56. The system according to any of the preceding embodiments, wherein the at least one agricultural machine is harvesting combine.

[0429] 57. The system according to any of the preceding embodiments, wherein the at least one agricultural machine is a self-propelled sprayer.

[0430] 58. The system according to any of the preceding embodiments, wherein the at least one agricultural machine is a self-driving sprayer.

[0431] 59. The system according to any of the preceding embodiments, wherein the at least one agricultural machine comprises a first agricultural machine and second agricultural machine, wherein the first agricultural machine is configured to be different form the second agricultural machine, and wherein the first agricultural machine and the second agricultural machine are configured to work, simultaneously, on the at least one agricultural zone.

[0432] 510. The system according to any of the preceding embodiments, wherein the at least one agricultural machine is in operation.

[0433] 511. The system according to any of the preceding embodiments, wherein the sensor module is configured to be installed on the at least one agricultural machine.

[0434] 512. The system according to any of the preceding embodiments, wherein the system comprises at least a communication module.

[0435] 513. The system according to any of the preceding embodiments, wherein the system comprises at least a server. 514. The system according to any of the preceding embodiments, wherein the system comprises at least a storing module.

[0436] 515. The system according to any of the preceding embodiments, with the features of embodiment S12, wherein the communication module is configured to be installed on the at least one agricultural machine.

[0437] 516. The system according to any of the preceding embodiments, with the features of embodiment S12, wherein the communication module is connected to the internet.

[0438] 517. The system according to any of the preceding embodiments, with the features of embodiments S12 and S13, wherein the communication module is configured to transmit the data related to at least an agricultural machine from the sensor module to the server.

[0439] 518. The system according to any of the preceding embodiments, with the features of embodiment S13, wherein the server is configured to receive the data related to at least an agricultural machine.

[0440] 519. The system according to any of the preceding embodiments, with the features of embodiments S13 and S14, wherein the server is configured to bidirectionally communicate with the storing module.

[0441] 520. The system according to any of the preceding embodiments, with the features of embodiments S12 and S14, wherein the storing module is configured to store at least in part the data related to at least an agricultural machine.

[0442] 521. The system according to any of the preceding embodiments, with the features of embodiment S13, wherein the server is configured to bidirectionally communicate with the analyzing module and / or with the processing module.

[0443] 522. The system according to any of the preceding embodiments, with the features of embodiment S14, wherein the storing module is configured to store, at least in part, at least an output generated by the analyzing module and / or by the processing module.

[0444] 523. The system according to any of the preceding embodiments, wherein the system comprises at least an interface module. 524. The system according to any of the preceding embodiments, wherein the analyzing module comprises an artificial intelligence module configured to execute at least an artificial intelligence algorithm.

[0445] 525. The system according to any of the preceding embodiments, with the features of embodiment S24, wherein the artificial intelligence module is configured to be trained at least in part on the data related to at least an agricultural machine and / or on the at least one operational attribute.

[0446] 526. The system according to any of the preceding embodiments, with the features of embodiments S14 and S24, wherein the artificial intelligence module is configured to be trained at least in part on data stored in the storing module.

[0447] 527. The system according to any of the preceding embodiments, with the features of embodiment S24, wherein the artificial intelligence module is configured to be trained at least in part on historical data.

[0448] 528. The system according to any of the preceding embodiments, with the features of embodiment S24, wherein the artificial intelligence module is configured to use at least one among: at least a supervised learning model, at least a unsupervised learning model, at least a reinforcement learning model, at least a generative module, at least a natural language processing model, at least a computer vision mode, at least a time series analysis model.

[0449] 529. The system according to any of the preceding embodiments, with the features of embodiment S23, wherein the interface module is configured to utilize a plurality of software interfaces with different levels.

[0450] 530. The system according to any of the preceding embodiments, with the features of embodiment S23, wherein the interface module is configured to provide, to an authorized user, access to any of the modules according to any of the preceding embodiments.

[0451] 531. The system according to any of the preceding embodiments, with the features of embodiment S23, wherein the interface module is configured to communicate with any combination of modules according to any of the preceding embodiments, said combination of modules comprising at least one module. 532. The system according to any of the preceding embodiments, with the features of embodiment S23, wherein the interface module is configured to change at least one parameter related to any combination of the modules according to any of the preceding system embodiments, said combination of modules comprising at least one module.

[0452] 533. The system according to any of the preceding embodiments, with the features of embodiment S24, wherein the artificial intelligence module is configured to be trained at least in part based on at least an input of data to the artificial intelligence module from the interface module.

[0453] 534. The system according to any of the preceding embodiments, wherein the system comprises at a modelling module.

[0454] 535. The system according to any of the preceding embodiments, with the features of embodiment S34, wherein the modelling module is configured to receive at least an output from the analyzing module and / or the processing module.

[0455] 536. The system according to any of the preceding embodiments, with the features of embodiments S14 and S34, wherein the modelling module is configured to access data stored in the storing module.

[0456] 537. The system according to any of the preceding embodiments, with the features of embodiment S34, wherein the modelling module is configured to generate a model based at least in part on an output from the analyzing module and / or the processing module.

[0457] 538. The system according to any of the preceding embodiments, with the features of embodiments S14 and S34, wherein the modelling module is configured to generate a model based at least in part data stored in the storing module.

[0458] 539. The system according to any of the preceding embodiments, wherein the system is configured to arrange locally or remotely one or more among: the communication module, the analyzing module, the processing module, the server, the storing module, the interface module, the modelling module.

[0459] 540. The system according to any of the preceding embodiments, wherein the system comprises at least a collective module, the collective module comprising the combination of any of the modules, according to any of the preceding system embodiments, among: the communication module, the analyzing module, the processing module, the server, the storing module, the interface module.

[0460] The system according to any of the preceding embodiments, wherein the system is configured to utilize different software for different purposes within a single module.

[0461] The system according to any of the preceding embodiments, wherein any of the modules according to any of the preceding system embodiments comprises a computing device.

[0462] The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to at least a task of the at least one agricultural machine.

[0463] The system according to any of the preceding embodiments, wherein the sensor module is configured to acquire data related to at least a task of the at least an agricultural machine, such that the task includes plowing and / or cultivating and / or disc cultivating and / or pi cultivating and / or harrowing and / or tilling and / or planting and / or fertilizing and / or spraying and / or harvesting and / or baling and / or mowing and / or loading and / or transporting and / or irrigating and / or raking and / or spreading manure and / or removing snow and / or regular drilling and / or direct drilling and / or liming.

[0464] The system according to any of the preceding embodiments, wherein the sensor module is configured to acquire data utilizing at least a position sensor.

[0465] The system according to any of the preceding embodiments, wherein the sensor module is configured to acquire data utilizing at least a time sensor.

[0466] The system according to any of the preceding embodiments, wherein the sensor module is configured to acquire data utilizing at least a positioning system.

[0467] The system according to any of the preceding embodiments, wherein the sensor module is configured to acquire data utilizing at least a satellite navigation system.

[0468] The system according to any of the preceding embodiments, wherein the sensor module is configured to acquire data utilizing at least a GPS navigation system. The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to the location of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0469] The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to the time of work of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0470] The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to the instant speed of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0471] The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to a log of locations of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0472] The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to a log of time of work of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0473] The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to the average speed of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0474] The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to the work range of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0475] The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to the execution period of the work of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least an agricultural machine.

[0476] 558. The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to the season of the year when the at least one agricultural machine works, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0477] 559. The system according to any of the preceding embodiments, with the features of any of embodiments S50, S51, S55, S56, S57, wherein the data related to the at least one agricultural machine comprises data related to the location and / or to the time of work and / or to the average speed and / or to the work range and / or to the execution period of the work of the at least one agricultural machine acquired by using at least a satellite navigation system.

[0478] 560. The system according to any of the preceding embodiments, with the features of embodiments S50 or S51 or S55 or S56 or S57, wherein the data related to the at least one agricultural machine comprises data related to the location and / or to the time of work and / or to the average speed and / or to the work range and / or to the execution period of the work of the at least one agricultural machine acquired by using at least a position sensor and / or a time sensor.

[0479] 561. The system according to any of the preceding embodiments, wherein the sensor module is configured to acquire data utilizing at least a weather sensor.

[0480] 562. The system according to any of the preceding embodiments, wherein the sensor module is configured to acquire data utilizing an internet connection.

[0481] 563. The system according to any of the preceding embodiments, with the features of embodiment S12, wherein the sensor module is configured to acquire data from the internet via the communication module.

[0482] 564. The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to the weather conditions when the at least one agricultural machine works, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0483] 565. The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to the temperature when the at least one agricultural machine works, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0484] 566. The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to the amount of rain in the period of time when the at least one agricultural machine works, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0485] 567. The system according to any of the preceding embodiments, wherein the sensor module is configured to acquire data utilizing at least an image recording device.

[0486] 568. The system according to any of the preceding embodiments, wherein the sensor module is configured to acquire data utilizing at least a camera.

[0487] 569. The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to at least one image of at least a part of the agricultural zone the at least one agricultural machine works on.

[0488] 570. The system according to any of the preceding embodiments, with the features of embodiment S69, wherein said at least one image is an image of at least a crop in the agricultural zone.

[0489] 571. The system according to any of the preceding embodiments, with the features of embodiment S69, wherein said at least one image is an image of a field ground of the agricultural zone.

[0490] 572. The system according to any of the preceding embodiments, with the features of embodiment S69, wherein said at least one image is an image of an implement used by the at least one agricultural machine. The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to the crop that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0491] The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to at least a crop type that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0492] The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to at least a cover crop and / or of a cover crop type and / or a plant and / or a biomass that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0493] The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to at least a cover crop biomass that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0494] The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to at least a crop yield of a crop that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0495] The system according to any of the preceding embodiments, wherein the sensor module is configured to acquire data utilizing at least a Bluetooth device.

[0496] The system according to any of the preceding embodiments, with the features of embodiment S78, wherein the sensor module is configured to acquire data utilizing at least a Bluetooth device mounted on the at least one agricultural machine and at least a corresponding Bluetooth device mounted on at least an implement used by the at least one agricultural machine. The system according to any of the preceding embodiments, wherein the sensor module is configured to establish a communication between the at least one agricultural machine and at least one implement mounted on the at least one agricultural machine.

[0497] The system according to any of the preceding embodiments, wherein the sensor module is configured to establish a wireless communication between the at least one agricultural machine and at least one implement mounted on the at least one agricultural machine.

[0498] The system according to any of the preceding embodiments, wherein the sensor module is configured to establish a wired communication between the at least one agricultural machine and at least one implement mounted on the at least one agricultural machine.

[0499] The system according to any of the preceding embodiments, wherein the sensor module is configured to establish a Bluetooth communication between at least a Bluetooth device mounted on the at least one agricultural machine and at least a Bluetooth device mounted on at least one implement used by the at least one agricultural machine.

[0500] The system according to any of the preceding embodiments, wherein the sensor module is configured to acquire data related to at least an implement used by the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least an agricultural machine.

[0501] The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to the type of at least an implement used by the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least an agricultural machine.

[0502] The system according to any of the preceding embodiments, wherein the data related to the at least one agricultural machine comprises data related to the dimension of at least an implement used by the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least an agricultural machine. 587. The system according to any of the preceding embodiments, wherein the at least one operational attribute comprises a location of a task of the at least one agricultural machine.

[0503] 588. The system according to any of the preceding embodiments, with the features of embodiments S50 and S87, wherein the analyzing module is configured to determine the location of a task of the at least one agricultural machine based at least in part on data related to the location of the at least one agricultural machine.

[0504] 589. The system according to any of the preceding embodiments, wherein the at least one operational attribute comprises a starting and / or ending and / or pausing time of a task of the at least one agricultural machine.

[0505] 590. The system according to any of the preceding embodiments, with the features of embodiments S51 and S89, wherein the analyzing module is configured to determine the starting and / or ending and / or pausing time of a task of the at least one agricultural machine based at least in part on data related to the time of work of the at least one agricultural machine.

[0506] 591. The system according to any of the preceding embodiments, with the features of embodiment S87, wherein the system is configured to use an agricultural zone as the location of a task of the at least one agricultural machine.

[0507] 592. The system according to any of the preceding embodiments, with the features of embodiment S87, wherein the system is configured to use an agricultural field as the location of a task of the at least one agricultural machine.

[0508] 593. The system according to any of the preceding embodiments, with the features of embodiment S87, wherein the location of a task of the at least one agricultural machine comprises a position of the at least one agricultural machine, when the at least one agricultural machine executes the task.

[0509] 594. The system according to any of the preceding embodiments, with the features of embodiment S93, wherein the analyzing module is configured to determine the position of the at least one agricultural machine based at least in part on data related to the location of the at least one agricultural machine.

[0510] 595. The system according to any of the preceding embodiments, with the features of embodiment S87, wherein the analyzing module is configured to compare data related to the location of the at least one agricultural machine to reference coordinates, wherein said reference coordinates define the perimeter of an agricultural zone.

[0511] 596. The system according to any of the preceding embodiments, with the features of embodiment S95, wherein the analyzing module is configured to identify if the at least one agricultural machine is located within the perimeter of the agricultural zone.

[0512] 597. The system according to any of the preceding embodiments, with the features of embodiments S89 and S95, wherein the analyzing module is configured to identify the start of a task of the at least one agricultural machine when the at least one agricultural machine switches from being located out of the perimeter of the agricultural zone to being located within the perimeter of the agricultural zone.

[0513] 598. The system according to any of the preceding embodiments, with the features of embodiments S89 and S95, wherein the analyzing module is configured to identify the end and / or the pause of a task of the at least one agricultural machine when the at least one agricultural machine switches from being located within the perimeter of the agricultural zone to being located out of the perimeter of the agricultural zone.

[0514] 599. The system according to any of the preceding embodiments, with the features of embodiment S87, wherein the analyzing module is configured to keep track of the position of the at least one agricultural machine from a first time to a second time based at least in part on data related to the location of the at least one agricultural machine and at least in part on data related to the time of work of the at least one agricultural machine.

[0515] 5100. The system according to any of the preceding embodiments, with the features of embodiment S93, wherein the analyzing module is configured to add a tolerance radius to the position of the at least one agricultural machine.

[0516] 5101. The system according to any of the preceding embodiments, with the features of embodiment S100, wherein the tolerance radius is set based on a detected implement. S102. The system according to any of the preceding embodiments, with the features of embodiment S100, wherein the tolerance radius is smaller or equal to 5 m, preferably smaller or equal to 10 m, more preferably smaller or equal to 30 m.

[0517] 5103. The system according to any of the preceding embodiments, with the features of embodiments S89 and S96 and S99, wherein the analyzing module is configured to identify the end of a task of the at least one agricultural machine, when the at least one agricultural machine switches from being located within the perimeter of the agricultural zone to being located out of the perimeter of the agricultural zone, and when the tracked position of the at least one agricultural machine covers more than a threshold portion of the agricultural zone.

[0518] 5104. The system according to any of the preceding embodiments, with the features of embodiment S103, wherein the analyzing module is configured to identify the start of a new task of the at least one agricultural machine after the end of a task of the at least one agricultural machine.

[0519] 5105. The system according to any of the preceding embodiments, with the features of embodiments S89 and S95 and S99, wherein the analyzing module is configured to identify a pause of a task of the at least one agricultural machine, when the at least one agricultural machine switches from being located within the perimeter of the agricultural zone to being located out of the perimeter of the agricultural zone, and when the tracked position of the at least one agricultural machine covers essentially less than threshold portion of the agricultural zone.

[0520] 5106. The system according to any of the preceding embodiments, with the features of embodiment S105, wherein the analyzing module is configured to delete the task and / or mark the task as ended and / or merge the task with another task of the at least one agricultural machine, after having identified the pause of said task.

[0521] 5107. The system according to any of the preceding embodiments, with the features of embodiments S23 and S105, wherein the analyzing module is configured to delete the task and / or mark the task as ended and / or merge the task with another task of the at least one agricultural machine, after having identified the pause said task, based at least in part on an input from the interface module.

[0522] 5108. The system according to any of the preceding embodiments, with the features of embodiments S103 or S105, wherein the analyzing module is configured to set the threshold portion of the agricultural zone amount to 60% of the area of the agricultural zone, preferably to 80% of the area of the agricultural zone, more preferably to 95% of the area of the agricultural zone.

[0523] S109. The system according to any of the preceding embodiments, with the features of embodiments S24 and S103 or S105, wherein the artificial intelligence module is configured to determine the threshold portion based at least in part on the training of the artificial intelligence.

[0524] SI 10. The system according to any of the preceding embodiments, with the features of embodiments S103 or S105, wherein the analyzing module is configured to update the threshold portion.

[0525] Sill. The system according to any of the preceding embodiments, with the features of embodiments S23 and S103 or S105, wherein the analyzing module is configured to update the threshold portion based at least in part on an input coming from the interface module.

[0526] SI 12. The system according to any of the preceding embodiments, with the features of embodiment S105, wherein the analyzing module is configured to determine an end of the task of the at least one agricultural machine after having identified the pause of said task, if the state of pause of said task persists for more than a threshold of time.

[0527] SI 13. The system according to any of the preceding embodiments, with the features of embodiment S105, wherein the analyzing module is configured to determine an end of the task of the at least one agricultural machine on an agricultural zone after having identified the pause of said task, when said task is taken over by another agricultural machine on the same agricultural zone.

[0528] SI 14. The system according to any of the preceding embodiments, with the features of embodiment S105, wherein the analyzing module is configured to identify a start of a continuation of the task of the at least one agricultural machine after having identified the pause of the task of the at least one agricultural machine, when the at least one agricultural machine switches from being located out of the perimeter of the agricultural zone to being located within the perimeter of the agricultural zone, wherein said switching happens after the analyzing module identifies the pause of the task of the at least one agricultural machine. SI 15. The system according to any of the preceding embodiments, with the features of embodiment SI 14, wherein the analyzing module is configured to determine an end of the task of the at least one agricultural machine after having identified the continuation of said task, when another task is started by the at least one agricultural machine on the agricultural zone.

[0529] SI 16. The system according to any of the preceding embodiments, with the features of embodiments S32 and S89, wherein the analyzing module is configured to determine the start and / or pause and / or end of a task of the at least one agricultural machine based on an input to the analyzing module from the interface module.

[0530] SI 17. The system according to any of the preceding embodiments, wherein the at least one operational attribute comprises at least a type of a task of the at least one agricultural machine.

[0531] SI 18. The system according to any of the preceding embodiments, with the features of embodiments S55 and SI 17, wherein the analyzing module is configured to determine at least a type of a task of the at least one agricultural machine based at least in part on data related to the average speed of the at least one agricultural machine.

[0532] SI 19. The system according to any of the preceding embodiments, with the features of embodiments S55 and SI 17, wherein the analyzing module is configured to determine at least a type of a task of the at least one agricultural machine based at least in part on data related to the location and / or to the time of work of the at least one agricultural machine.

[0533] 5120. The system according to any of the preceding embodiments, with the features of embodiment S55, wherein the analyzing module is configured to relate data related to the average speed of the at least one agricultural machine to at least an implement used by the at least one agricultural machine.

[0534] 5121. The system according to any of the preceding embodiments, with the features of embodiments S56 and SI 17, wherein the analyzing module is configured to determine at least a type of a task of the at least one agricultural machine based at least in part on data related to the work range of the at least one agricultural machine. 5122. The system according to any of the preceding embodiments, with the features of embodiment S56, wherein the analyzing module is configured to relate data related to the work range of the at least one agricultural machine to at least an implement used by the at least one agricultural machine.

[0535] 5123. The system according to any of the preceding embodiments, with the features of embodiments S57 and SI 17, wherein the analyzing module is configured to determine at least a type of a task of the at least one agricultural machine based at least in part on data related to the execution period of the work of the at least one agricultural machine.

[0536] 5124. The system according to any of the preceding embodiments, with the features of embodiment S57, wherein the analyzing module is configured to relate data related to the execution period of the work of the at least one agricultural machine to at least an implement used by the at least one agricultural machine.

[0537] 5125. The system according to any of the preceding embodiments, with the features of embodiments S64 and SI 17, wherein the analyzing module is configured to determine at least a type of a task of the at least one agricultural machine based at least in part on data related to the weather conditions when the at least one agricultural machine works.

[0538] 5126. The system according to any of the preceding embodiments, with the features of embodiment S64, wherein the analyzing module is configured to relate data related to the weather conditions when the at least one agricultural machine works to at least an implement used by the at least one agricultural machine.

[0539] 5127. The system according to any of the preceding embodiments, wherein the analyzing module is configured to determine at least a type of a task of the at least one agricultural machine based at least in part on the implement that the at least one agricultural machine uses.

[0540] 5128. The system according to any of the preceding embodiments, with the features of embodiments S55 and SI 17, wherein the analyzing module is configured to determine the at least one type of task of the at least one agricultural machine based on at least a logical relation between the average speed of the at least one agricultural machine and the at least one type of task of the at least one agricultural machine. 5129. The system according to any of the preceding embodiments, with the features of embodiment S117, wherein the analyzing module is configured to assign a range of average speed of the at least one agricultural machine to the at least one type of task that the at least one agricultural machine executes at said average speed.

[0541] 5130. The system according to any of the preceding embodiments, with the features of embodiments S55 and S129, wherein the analyzing module is configured to compare the data related to the average speed of the at least one agricultural machine to the range of average speed assigned to the at least one type task of the at least one agricultural machine.

[0542] 5131. The system according to any of the preceding embodiments, with the features of embodiment S130, wherein the analyzing module is configured to determine the at least one of type task of the at least one agricultural machine if the data related to the average speed of the at least one agricultural machine is compatible with the range of average speed assigned to the at least one type task of the at least one agricultural machine.

[0543] 5132. The system according to any of the preceding embodiments, with the features of embodiments S56 and SI 17, wherein the analyzing module is configured to determine the at least one type of task of the at least one agricultural machine based on at least a logical relation between the work range of the at least one agricultural machine and the at least one type of task of the at least one agricultural machine.

[0544] 5133. The system according to any of the preceding embodiments, with the features of embodiment SI 17, wherein the analyzing module is configured to assign a span of work range of the at least one agricultural machine to the at least one type of task that the at least one agricultural machine executes at said average speed.

[0545] 5134. The system according to any of the preceding embodiments, with the features of embodiment S133, wherein the analyzing module is configured to compare the data related to the work range of the at least one agricultural machine to the span of work range assigned to the at least one type task of the at least one agricultural machine.

[0546] 5135. The system according to any of the preceding embodiments, with the features of embodiments S56 and S134, wherein the analyzing module is configured to identify the at least one type of task of the at least one agricultural machine if the data related to the work range of the at least one agricultural machine is compatible with the span of work range assigned to the at least one type task of the at least one agricultural machine.

[0547] 5136. The system according to any of the preceding embodiments, with the features of embodiments S56 and SI 17, wherein the analyzing module is configured to identify the at least one type of task of the at least one agricultural machine based on at least a logical relation between the execution period of the work of the at least one agricultural machine and the at least one type of task of the at least one agricultural machine.

[0548] 5137. The system according to any of the preceding embodiments, with the features of embodiment S117, wherein the analyzing module is configured to assign a range of execution period of the work of the at least one agricultural machine to the at least one type of task that the at least one agricultural machine executes at said average speed.

[0549] 5138. The system according to any of the preceding embodiments, with the features of embodiments S57 and S137, wherein the analyzing module is configured to compare the data related to the execution period of the work of the at least one agricultural machine to the range of execution period of the work assigned to the at least one type task of the at least one agricultural machine.

[0550] 5139. The system according to any of the preceding embodiments, with the features of embodiment S138, wherein the analyzing module is configured to identify the at least one type of task of the at least one agricultural machine if the data related to the range of execution period of the at least one agricultural machine is compatible with range of execution period of the work assigned to the at least one type task of the at least one agricultural machine.

[0551] 5140. The system according to any of the preceding embodiments, with the features of embodiments S64 and SI 17, wherein the analyzing module is configured to identify the at least one type of task of the at least one agricultural machine based on at least a logical relation between the weather conditions when the at least one agricultural machine works and the at least one type of task of the at least one agricultural machine.

[0552] 5141. The system according to any of the preceding embodiments, with the features of embodiment S117, wherein the analyzing module is configured to assign a range of weather conditions of the at least one agricultural machine to the at least one type of task that the at least one agricultural machine executes at said average speed.

[0553] 5142. The system according to any of the preceding embodiments, with the features of embodiments S64 and S141, wherein the analyzing module is configured to compare the data related to the weather conditions when the at least one agricultural machine works to the range of weather conditions assigned to the at least one type task of the at least one agricultural machine.

[0554] 5143. The system according to any of the preceding embodiments, with the features of embodiment S142, wherein the analyzing module is configured to identify the at least one type of task of the at least one agricultural machine if the data related to the weather conditions when the at least one agricultural machine works is compatible with range weather conditions assigned to the at least one type task of the at least one agricultural machine.

[0555] 5144. The system according to any of the preceding embodiments, wherein the analyzing module is configured to identify the at least one type of task of the at least one agricultural machine based on at least a logical relation between at least an implement that the at least one agricultural machine uses and at least a type of a task of the at least one agricultural machine.

[0556] 5145. The system according to any of the preceding embodiments, with the features of embodiment S32, wherein the analyzing module is configured to determine the ranges, according to embodiments S129 and / or S133 and / or S137 and / or S141, based on an input to the analyzing module from the interface module.

[0557] 5146. The system according to any of the preceding embodiments, with the features of embodiment S32 wherein the analyzing module is configured to determine the at least one logical relation, according to embodiments S128 and / or S132 and / or S136 and / or S140 and / or S144, based on an input to the analyzing module from the interface module.

[0558] 5147. The system according to any of the preceding embodiments, with the features of embodiments S25 and / or S26 and / or S33, wherein the artificial intelligence module is configured to determine the ranges, according to embodiments S129 and / or S133 and / or S137 and / or S141, based on the training of the artificial intelligence. 5148. The system according to any of the preceding embodiments, with the features of embodiments S25 and / or S26 and / or S33, wherein, particularly, the artificial intelligence module is configured to determine the at least one logical relation, according to embodiments S128 and / or S132 and / or S136 and / or S140 and / or S144, based on the training of the artificial intelligence.

[0559] 5149. The system according to any of the preceding embodiments, with the features of embodiments S69 and SI 17, wherein the analyzing module is configured to determine the at least one type of a task of the at least one agricultural machine based at least in part on data related to the image of at least a part of the agricultural zone the at least one agricultural machine works on.

[0560] 5150. The system according to any of the preceding embodiments, with the features of embodiments S69 and SI 17 and S25 and / or S26 and / or S33, wherein the artificial intelligence module is configured to determine the at least one type of a task of the at least one agricultural machine based at least in part on data related to the image of at least a part of the agricultural zone the at least one agricultural machine works on, and / or based on the training of the artificial intelligence.

[0561] 5151. The system according to any of the preceding embodiments, with the features of embodiments S69 and S25 and / or S26 and / or S33, wherein the artificial intelligence module is configured to determine at least a crop type and / or cover crop and / or cover crop type and / or crop existence and / or cover crop existence and / or cover crop biomass and / or plant biomass and / or yield and / or an implement and / or a crop yield and / or the like based at least in part on data related to the image of at least a part of the agricultural zone the at least one agricultural machine works on, and / or based on the training of the artificial intelligence.

[0562] 5152. The system according to any of the preceding embodiments, with the features of embodiments S83 and SI 17, wherein the analyzing module is configured to determine at least a type of a task of the at least one agricultural machine based at least in part on at least a type of implement used by the at least one agricultural machine, wherein the at least one type of implement is recognized by means of the Bluetooth connection between the at least one Bluetooth device mounted on the at least one agricultural machine and the at least one Bluetooth device mounted on at least one implement used by the at least one agricultural machine. 5153. The system according to any of the preceding embodiments, wherein the at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine comprises a report on the utilization of an agricultural zone used by the at least one agricultural machine.

[0563] 5154. The system according to any of the preceding embodiments, with the features of embodiment S153, wherein the report is adapted for governmental reporting.

[0564] 5155. The system according to any of the preceding embodiments, with the features of embodiment S153, wherein the report is adapted for insurance reporting.

[0565] 5156. The system according to any of the preceding embodiments, with the features of embodiment S153, wherein the report is adapted for sustainable loan reporting.

[0566] 5157. The system according to any of the preceding embodiments, with the features of embodiment S153, wherein the report is adapted for soil organic carbon reporting and / or greenhouse gas emission reporting and / or carbon program reporting.

[0567] 5158. The system according to any of the preceding embodiments, with the features of embodiment S153, wherein the report comprises fields.

[0568] 5159. The system according to any of the preceding embodiments, wherein the artificial intelligence module is configured to analyze the at least one the at least one data indicative of the utilization of at least an agricultural zone according to a standardized database.

[0569] 5160. The system according to any of the preceding embodiments, with the features of embodiment S158, wherein said fields relate at least in part to the location of a task of the at least one agricultural machine.

[0570] 5161. The system according to any of the preceding embodiments, with the features of embodiment S158, wherein said fields relate at least in part to the starting and / or ending and / or pausing time of a task of the at least one agricultural machine.

[0571] S162. The system according to any of the preceding embodiments, with the features of embodiment S158, wherein said fields relate at least in part to the type of a task of the at least one agricultural machine. 5163. The system according to any of the preceding embodiments, wherein the at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine comprises an estimation of a change in greenhouse gas emission related at least in part to the at least one agricultural zone.

[0572] 5164. The system according to any of the preceding embodiments, wherein the at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine comprises an estimation of a greenhouse gas emission related at least in part to the at least one agricultural zone.

[0573] 5165. The system according to any of the preceding embodiments, wherein the at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine comprises an estimation of a greenhouse gas emission related at least in part to the at least one agricultural zone.

[0574] 5166. The system according to any of the preceding embodiments, wherein the at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine comprises an estimation of a balance of soil organic carbon related at least in part to the at least one agricultural zone.

[0575] 5167. The system according to any of the preceding embodiments, wherein the at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine comprises an estimation of a change of soil organic carbon related at least in part to the at least one agricultural zone.

[0576] 5168. The system according to any of the preceding embodiments, wherein the at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine comprises an estimation of carbon sequestration related at least in part to the at least one agricultural zone.

[0577] 5169. The system according to any of the preceding embodiments, wherein the system is configured to generate a soil organic carbon model and / or a greenhouse gas emission model and / or a carbon sequestration model related to at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0578] S170. The system according to any of the preceding embodiments, with the features of any of embodiments S35, S36, S37, and S38, wherein the modelling module is configured to generate a soil organic carbon model and / or a greenhouse gas emission model and / or a carbon sequestration model related to at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0579] 5171. The system according to any of the preceding system embodiments, with the features of any of embodiments S169 and S170, wherein the system is configured to train at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine based on said model.

[0580] 5172. The system according to any of the preceding embodiments, wherein the system is a system for optimizing the utilization of at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0581] 5173. The system according to any of the preceding embodiments, wherein the system is a system for generating at least a report, said report concerning at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0582] 5174. The system according to any of the preceding embodiments, wherein the system is a system for calculating soil organic carbon change, said change concerning at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0583] 5175. The system according to any of the preceding embodiments, wherein the system is a system for calculating and / or modelling greenhouse gas emissions, said greenhouse gas emissions concerning at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0584] 5176. The system according to any of the preceding embodiments, wherein the system is a system for optimizing carbon sequestration, said carbon sequestration concerning at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0585] 5177. The system according to any of the preceding embodiments, wherein the system is configured to train and / or optimize the at least one agricultural zone and / or the usage of said agricultural zone by the at least one agricultural machine based on the at least one operational attribute. 5178. The system according to any of the preceding embodiments, with the features of S177, wherein, in said training and / or optimization, the system, preferably via the interface module, is configured to output at least a suggestion on the use of the at least one agricultural machine such as to reduce the difference between the at least one data indicative of the utilization of the at least one agricultural zone and at least a standardized value and / or a threshold value.

[0586] 5179. The system according to any of the preceding embodiments, with the features of S178 and S23, wherein the interface module is configured to receive from an authorized user said threshold value.

[0587] 5180. The system according to any of the preceding embodiments, with the features of S178 and S24, wherein the artificial intelligence module is configured to determine said suggestion based at least in part on the training of the artificial intelligence module.

[0588] 5181. The system according to any of the preceding embodiments, with the features of S23, wherein the interface module is configured to receive user training data for the artificial intelligence module from an authorized user.

[0589] 5182. The system according to any of the preceding embodiments, with the features of 524 and S181, wherein the artificial intelligence module is configured to be trained on said user training data, wherein said user training data comprise at least an image of at least an implement that the at least one agricultural machine may use and the type of task of the at least one agricultural machine corresponding to said implement.

[0590] 5183. The system according to any of the preceding embodiments, with the features of 525 and S181, wherein the artificial intelligence module is configured to be trained on the data related to at least an agricultural machine, preferably image data of at least an implement that the at least one agricultural machine may use, and on said user training data, wherein said user training data comprises the type of said implement and / or the type of task of the at least one agricultural machine corresponding to said implement.

[0591] S184. The system according to any of the preceding embodiments, with the features of S25, wherein the artificial intelligence module is configured to determine a type of task of the at least one agricultural machine and / or the type of implement used by the at least one agricultural machine before the at least one agricultural machine enters an agricultural field and / or an agricultural zone.

[0592] 5185. The system according to any of the preceding embodiments, with the features of S23, S67, and S68, wherein the artificial intelligence module is configured to recognize and censor faces in the data acquired by the at least one image recording device and / or by the at least one camera.

[0593] 5186. The system according to any of the preceding embodiments, with the features of S23, S67, and S68, wherein the artificial intelligence module is configured to recognize faces in the image data acquired by the at least one image recording device and / or by the at least one camera and delete said data.

[0594] 5187. The system according to any of the preceding embodiments, with the features of 523, S67, and S68, wherein the artificial intelligence module is configured to recognize if the image data acquired by the at least one image recording device and / or by the at least one camera does not relate, at least in part, to an agricultural zone and / or to an agricultural field, and delete said data.

[0595] 5188. The system according to any of the preceding embodiments, with the features of 524, S67, and S68, wherein the analyzing module, preferably the artificial intelligence module, is configured to determine, based on data acquired by the at least one image recording device and / or by the at least one camera, the type of product that is loaded on an implement that the at least one agricultural machine uses, when the product is being loaded and / or when the at least one agricultural machine uses the implement with the product.

[0596] 5189. The system according to any of the preceding embodiments, with the features of S24, wherein the analyzing module, preferably the artificial intelligence module, is configured to send an intervention prompt to an authorized user, preferably via the interface module.

[0597] 5190. The system according to any of the preceding embodiments, with the features of S189, wherein, in the intervention prompt, the system, preferably via the interface module, is configured to output at least a request of input to an authorized user, the input relating to the current and / or future use of an implement of the at least one agricultural machine. 5191. The system according to any of the preceding embodiments, with the features of S189, wherein, in the intervention prompt, the system, preferably via the interface module, is configured to output at least a request of intervention on the sensor module to an authorized user.

[0598] 5192. The system according to any of the preceding embodiments, with the features of S67 and S68, wherein the at least on image recording device and / or the at least one camera is secured to a transparent panel of the at least one agricultural machine.

[0599] 5193. The system according to any of the preceding embodiments, with the features of S67 and S68, wherein the at least on image recording device and / or the at least one camera is powered via a battery present on the at least one agricultural machine.

[0600] 5194. The system according to any of the preceding embodiments, with the features of S67 and S68, wherein the at least on image recording device and / or the at least one camera is powered via a 3-pin DIN connector.

[0601] 5195. The system according to any of the preceding embodiments, with the features of S67 and S68, wherein the at least on image recording device and / or the at least one camera is powered via a power socket present in the cabin of the at least one agricultural machine.

[0602] 5196. The system according to any of the preceding embodiments, with the features of S24, wherein the analyzing module, preferably the Al module, is configured to determine and / or update the at least one operational attribute at least when the at least one agricultural machine undergoes a task changing event.

[0603] 5197. The system according to any of the preceding embodiments, with the features of S24, wherein the artificial intelligence module is configured to determine a type of task of the at least one agricultural machine and / or the type of implement used by the at least one agricultural machine when the at least one agricultural machine undergoes a task changing event.

[0604] 5198. The system according to any of the preceding embodiments, with the features of S196, wherein the task changing event comprises the at least one of: the at least one agricultural machine leaving a task changing zone, the at least one agricultural machine coming to a halt, the at least one agricultural machine turning off, the at least one agricultural machine entering and / or leaving the agricultural zone and / or agricultural area.

[0605] 5199. The system according to any of the preceding embodiments, with the features of S149, wherein, before determining the at least one type of task based on the data related to the image of at least a part of the agricultural zone, the analyzing module, preferably the Al module, is configured to iteratively determine the quality the data related to the image and prompt the sensor module to acquire new data until the determined quality is above a quality threshold.

[0606] 5200. The system according to any of the preceding embodiments, with the features of S14, wherein the storing module is, at least in part, remotely arranged and, at least in part, locally arranged on the at least one agricultural machine and wherein the system is configured to operate independently of the remotely arranged part of the storing module.

[0607] 5201. The system according to any of the preceding embodiments, with the features of S45 and / or S46 and / or S47 and / or S48 and / or S49 and / or S61 and / or S62, wherein the system is configured to acquire data utilizing at least one among the position sensor, the time sensor, the positioning system, the satellite navigation system, the GPS navigation system, the weather sensor, the internet connection at a frequency between 0.5 seconds and 5 minutes, preferably between 1 second and 1 minute, more preferably between 3 seconds and 7 seconds.

[0608] 5202. The system according to any of the preceding embodiments, with the features of S67 and / or S68, wherein the system is configured to acquire data utilizing at least one among the image recording device, the camera at an acquisition frequency between 30 seconds and 15 minutes, preferably between 1 minute and 10 minutes, more preferably between 3 minutes and 7 minutes.

[0609] 5203. The system according to any of the preceding embodiments, with the features of S45 and / or S46 and / or S47 and / or S48 and / or S49 and / or S61 and / or S62, wherein the system is configured to acquire data utilizing at least one among the position sensor, the time sensor, the positioning system, the satellite navigation system, the GPS navigation system, the weather sensor, the internet connection upon a trigger signal. S204. The system according to any of the preceding embodiments, with the features of S67 and / or S68, wherein the system is configured to acquire data utilizing at least one among the image recording device, the camera upon a trigger signal.

[0610] 5205. The system according to any of the preceding embodiments, with the features of S203 and / or S204, wherein the trigger signal is received, preferably by the interface module, from an authorized user.

[0611] 5206. The system according to any of the preceding embodiments, with the features of S203 and / or S204, wherein the trigger signal is remotely received, preferably by the interface module, from an authorized user.

[0612] 5207. The system according to any of the preceding embodiments, with the features of S198 and S203 and / or S204, wherein the trigger signal is received when the at least one agricultural machine undergoes a task changing event.

[0613] 5208. The system according to any of the preceding embodiments, with the features of S203 and / or S204, wherein the trigger signal relates to one or more events and / or conditions in the operation of the at least one agricultural machine.

[0614] 5209. The system according to any of the preceding embodiments, with the features of S23 and S201 and / or S202, wherein, the system, preferably via the interface module, is configured to allow an authorized user to change said acquisition frequency(ies).

[0615] 5210. The system according to any of the preceding embodiments, with the features of S24, wherein the analyzing module, preferably the artificial intelligence module, is configured to send an input prompt to an authorized user, preferably via the interface module.

[0616] 5211. The system according to any of the preceding embodiments, with the features of S210, wherein, in the input prompt, the system, preferably via the interface module, is configured to output at least a request of input to an authorized user, the input relating to the current and / or future use of an implement of the at least one agricultural machine and / or to the use of the at least one agricultural machine.

[0617] 5212. The system according to any of the preceding embodiments, with the features of S210, wherein the request of input is sent upon a triggering event. S213. The system according to any of the preceding embodiments, with the features of S212, wherein the triggering event relates to a change in the activity of the at least one agricultural machine

[0618] 5214. The system according to any of the preceding embodiments, with the features of S210, wherein the input prompt is a call carried out by the analyzing module, preferably by the Al module.

[0619] 5215. The system according to any of the preceding embodiments, with the features of S24, wherein the Al module is configured to run an Al agent.

[0620] 5216. The system according to any of the preceding embodiments, with the features of S214 and S215, wherein the call is run by the Al agent.

[0621] 5217. The system according to any of the preceding embodiments, with the features of S210 and / or S24, wherein the system, preferably the analyzing module, more preferably the Al module, is configured to store the input(s), preferably in the storing module.

[0622] 5218. The system according to any of the preceding embodiments, with the features of S210 and S24, wherein the Al module is configured to by trained on the input(s) stored.

[0623] 5219. The system according to any of the preceding embodiments, with the features of S24, wherein the Al module is configured to receive the data related to the at least one agricultural machine and build contextual awareness based thereon.

[0624] 5220. The system according to any of the preceding embodiments, with the features of S24, wherein the analyzing module, preferably the artificial intelligence module, is configured to send an alert, preferably via the interface module, based on an alert threshold.

[0625] 5221. The system according to any of the preceding embodiments, with the features of S220, wherein the alert threshold relates to a standardized use of the at least one agricultural machine and / or the agricultural field that the at least one agricultural machine works on. S222. The system according to any of the preceding embodiments, with the features of S23 and S220, wherein the interface module is configured to receive from an authorized user said alert threshold.

[0626] 5223. The system according to any of the preceding embodiments, with the features of S23 and S24, wherein the Al module is configured to determined said alert threshold.

[0627] 5224. The system according to any of the preceding embodiments, wherein the system is configured to send the data related to at least an agricultural machine to an external Al model for training the external Al model.

[0628] Below, method embodiments are presented. Method embodiments are abbreviated by the letter "M" followed by a number. Whenever reference is made herein to "method embodiments", these embodiments are meant.

[0629] Ml. A method comprising:

[0630] acquiring data related to at least an agricultural machine,

[0631] receiving the data related to at least an agricultural machine and determining at least an operational attribute of the at least one agricultural machine based on the data related to at least an agricultural machine,

[0632] generating at least a data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine based on the at least one operational attribute.

[0633] M2. The method according to the preceding method embodiment, wherein the method is a method for automatic agricultural data collection.

[0634] M3. The method according to any of the preceding method embodiments, wherein the method comprises using a stationary agricultural machine.

[0635] M4. The method according to any of the preceding method embodiments, wherein the method comprises using a movable agricultural machine.

[0636] M5. The method according to any of the preceding method embodiments, wherein the method comprises using a tractor.

[0637] M6. The method according to any of the preceding method embodiments, wherein the method comprises using a harvesting combine. M7. The method according to any of the preceding method embodiments, wherein the method comprises using a self-propelled sprayer.

[0638] M8. The method according to any of the preceding method embodiments, wherein the method comprises using a self-driving sprayer.

[0639] M9. The method according to any of the preceding method embodiments, wherein the method comprises using at least a first agricultural machine and at least a second agricultural machine, wherein the at least one first agricultural machine is configured to be different form the at least one second agricultural machine, and wherein the method comprises using the at least one first agricultural machine and the at least on second agricultural machine simultaneously, on the at least one agricultural zone.

[0640] MIO. The method according to any of the preceding method embodiments, wherein the at least one agricultural machine is in operation.

[0641] Mil. The method according to any of the preceding method embodiments, wherein the method comprises using at least a sensor module, wherein the method comprises installing the at least one sensor module on the at least one agricultural machine.

[0642] M12. The method according to any of the preceding method embodiments, wherein the method comprises using at least a communication module.

[0643] M13. The method according to any of the preceding method embodiments, wherein the method comprises using at least a server.

[0644] M14. The method according to any of the preceding method embodiments, wherein the method comprises using at least a storing module.

[0645] M15. The method according to any of the preceding method embodiments, with the features of embodiment M12, wherein the method comprises installing the communication module on the at least one agricultural machine.

[0646] M16. The method according to any of the preceding method embodiments, with the features of embodiment M12, wherein the method comprises connecting the communication module to the internet. M17. The method according to any of the preceding method embodiments with the features of embodiments M12 and M13, wherein the method comprises transmitting the data related to at least an agricultural machine from the sensor module to the server.

[0647] M18. The method according to any of the preceding method embodiments, with the features of embodiment M13, wherein the method comprises receiving data from the data related to at least an agricultural machine to the server.

[0648] M19. The method according to any of the preceding method embodiments, with the features of embodiments M13 and M14, wherein the method comprises bidirectionally communicating between the server and the storing module.

[0649] M20. The method according to any of the preceding method embodiments, with the features of embodiments M12 and M14, wherein the method comprises storing at least in part the data related to at least an agricultural machine in the storing module.

[0650] M21. The method according to any of the preceding method embodiments, with the features of embodiment M13, wherein the method comprises bidirectionally communicating between the server and the analyzing module and / or the processing module.

[0651] M22. The method according to any of the preceding method embodiments, with the features of embodiment M14, wherein the method comprises storing in the storing module, at least in part, at least an output generated by the analyzing module and / or by the processing module.

[0652] M23. The method according to any of the preceding method embodiments, wherein the method comprises using at least an interface module.

[0653] M24. The method according to any of the preceding method embodiments, wherein the method comprises using an artificial intelligence module and executing at least an artificial intelligence algorithm, particularly in the artificial intelligence module.

[0654] M25. The method according to any of the preceding method embodiments, with the features of embodiment M24, wherein the method comprises training the artificial intelligence module at least in part on the data related to at least an agricultural machine and / or on the at least one operational attribute. M26. The method according to any of the preceding method embodiments, with the features of embodiments M14 and M24, wherein the method comprises training the artificial intelligence module at least in part on data stored in the storing module.

[0655] M27. The method according to any of the preceding method embodiments, with the features of embodiment M24, wherein the method comprises training the artificial intelligence module at least in part on historical data.

[0656] M28. The method according to any of the preceding method embodiments, with the features of embodiment M24, wherein the method comprises using at least one among: at least a supervised learning model, at least a unsupervised learning model, at least a reinforcement learning model, at least a generative module, at least a natural language processing model, at least a computer vision mode, at least a time series analysis model.

[0657] M29. The method according to any of the preceding method embodiments, wherein the method comprises utilizing a plurality of software interfaces with different levels.

[0658] M30. The method according to any of the preceding method embodiments, wherein the method comprises providing, to an authorized user, access to any of the modules according to any of the preceding embodiments.

[0659] M31. The method according to any of the preceding method embodiments, wherein the method comprises communicating with any combination of modules according to any of the preceding embodiments, said combination of modules comprising at least one module.

[0660] M32. The method according to any of the preceding method embodiments, wherein the method comprises changing at least one parameter related to any combination of the modules according to any of the preceding embodiments, said combination of modules comprising at least one module.

[0661] M33. The method according to any of the preceding method embodiments, with the features of embodiment M24, wherein the method comprises training the artificial intelligence module at least in part based on at least an input of data to the artificial intelligence module from the interface module. M34. The method according to any of the preceding method embodiments, wherein the method comprises using a modelling module.

[0662] M35. The method according to any of the preceding method embodiments, wherein the method comprises receiving an output from an analyzing module and / or a processing module to the modelling module.

[0663] M36. The method according to any of the preceding method embodiments, with the features of embodiment M14, wherein the method comprises accessing data stored in the storing module.

[0664] M37. The method according to any of the preceding method embodiments, wherein the method comprises generating a model based at least in part on an output from an analyzing module and / or a processing module.

[0665] M38. The method according to any of the preceding method embodiments, with the features of embodiment M14, wherein the method comprises generating a model based at least in part data stored in the storing module.

[0666] M39. The method according to any of the preceding method embodiments, wherein the method comprises arranging locally or remotely one or more among: the communication module, the analyzing module, the processing module, the server, the storing module, the interface module, the modelling module.

[0667] M40. The method according to any of the preceding method embodiments, wherein the method comprises using at least a collective module, the collective module comprising the combination of any of the modules, according to any of the preceding embodiments, among: the communication module, the analyzing module, the processing module, the server, the storing module, the interface module.

[0668] M41. The method according to any of the preceding method embodiments, wherein the method comprises utilizing different software for different purposes within a single module.

[0669] M42. The method according to any of the preceding method embodiments, wherein the method comprises using a computing device, possibly in any of the modules according to any of the preceding embodiments. M43. The method according to any of the preceding method embodiments, the data related to the at least one agricultural machine comprises data related to at least a task of the at least one agricultural machine.

[0670] M44. The method according to any of the preceding method embodiments, wherein the method comprises acquiring data related to at least a task of the at least an agricultural machine, such that the task includes plowing and / or cultivating and / or disc cultivating and / or pi cultivating and / or harrowing and / or tilling and / or planting and / or fertilizing and / or spraying and / or harvesting and / or baling and / or mowing and / or loading and / or transporting and / or irrigating and / or raking and / or spreading manure and / or removing snow and / or regular drilling and / or direct drilling and / or liming.

[0671] M45. The method according to any of the preceding method embodiments, wherein the method comprises acquiring data utilizing at least a position sensor.

[0672] M46. The method according to any of the preceding method embodiments, wherein the method comprises acquiring data utilizing at least a time sensor.

[0673] M47. The method according to any of the preceding method embodiments, wherein the method comprises acquiring data utilizing at least a positioning system.

[0674] M48. The method according to any of the preceding method embodiments, wherein the method comprises acquiring data utilizing at least a satellite navigation system.

[0675] M49. The method according to any of the preceding method embodiments, wherein the method comprises acquiring data at least a GPS navigation system.

[0676] M50. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to the location of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0677] M51. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to the time of work of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine. M52. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to the instant speed of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0678] M53. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to a log of locations of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0679] M54. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to a log of time of work of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0680] M55. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to the average speed of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0681] M56. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to the work range of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0682] M57. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to the execution period of the work of the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least an agricultural machine.

[0683] M58. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to the season of the year when the at least one agricultural machine works, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine. M59. The method according to any of the preceding method embodiments, with the features of any of embodiments M50, M51, M55, M56, M57, wherein the data related to the at least one agricultural machine comprises data related to the location and / or to the time of work and / or to the average speed and / or to the work range and / or to the execution period of the work of the at least one agricultural machine acquired by using at least a satellite navigation system.

[0684] M60. The method according to any of the preceding method embodiments, with the features of any of embodiments M50, M51, M55, M56, M57, wherein the data related to the at least one agricultural machine comprises data related to the location and / or to the time of work and / or to the average speed and / or to the work range and / or to the execution period of the work of the at least one agricultural machine acquired by using at least a position sensor and / or a time sensor.

[0685] M61. The method according to any of the preceding method embodiments, wherein the method comprises acquiring data utilizing at least a weather sensor.

[0686] M62. The method according to any of the preceding method embodiments, wherein the method comprises acquiring data utilizing an internet connection.

[0687] M63. The method according to any of the preceding method embodiments, with the features of embodiment M12, wherein the method comprises acquiring data from the internet via the communication module.

[0688] M64. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to the weather conditions when the at least one agricultural machine works, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0689] M65. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to the temperature when the at least one agricultural machine works, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine. M66. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to the amount of rain in the period of time when the at least one agricultural machine works, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0690] M67. The method according to any of the preceding method embodiments, wherein the method comprises acquiring data utilizing at least an image recording device.

[0691] M68. The method according to any of the preceding method embodiments, wherein the method comprises acquiring data utilizing at least a camera.

[0692] M69. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to at least one image of at least a part of the agricultural zone the at least one agricultural machine works on.

[0693] M70. The method according to any of the preceding method embodiments, with the features of embodiment M69, wherein said at least one image is an image of at least a crop in the agricultural zone.

[0694] M71. The method according to any of the preceding method embodiments, with the features of embodiment M69, wherein said at least one image is an image of a field ground of the agricultural zone.

[0695] M72. The system according to any of the preceding embodiments, with the features of embodiment M69, wherein said at least one image is an image of an implement used by the at least one agricultural machine.

[0696] M73. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to the crop that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0697] M74. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to at least a crop type that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0698] M75. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to at least a cover crop and / or of a cover crop type and / or a plant and / or a biomass that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0699] M76. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to at least a cover crop biomass that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0700] M77. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to at least a crop yield of a crop that the at least one agricultural machine works on, preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

[0701] M78. The method according to any of the preceding method embodiments, wherein the method comprises acquiring data utilizing at least a Bluetooth device.

[0702] M79. The method according to any of the preceding method embodiments, with the features of embodiment M78, wherein the method comprises acquiring data utilizing at least a Bluetooth device mounted on the at least one agricultural machine and at least a corresponding Bluetooth device mounted on at least an implement used by the at least one agricultural machine.

[0703] M80. The method according to any of the preceding method embodiments, wherein the method comprises establishing a communication between the at least one agricultural machine and at least one implement mounted on the at least one agricultural machine.

[0704] M81. The method according to any of the preceding method embodiments, wherein the method comprises establishing a wireless communication between the at least one agricultural machine and at least one implement mounted on the at least one agricultural machine. M82. The method according to any of the preceding method embodiments, wherein the method comprises establishing a wired communication between the at least one agricultural machine and at least one implement mounted on the at least one agricultural machine.

[0705] M83. The method according to any of the preceding method embodiments, wherein the method comprises establishing a Bluetooth communication between at least a Bluetooth device mounted on the at least one agricultural machine and at least a Bluetooth device mounted on at least one implement used by the at least one agricultural machine.

[0706] M84. The method according to any of the preceding method embodiments, wherein the method comprises acquiring data related to at least an implement used by the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least an agricultural machine.

[0707] M85. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to the type of at least an implement used by the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least an agricultural machine.

[0708] M86. The method according to any of the preceding method embodiments, wherein the data related to the at least one agricultural machine comprises data related to the dimension of at least an implement used by the at least one agricultural machine, preferably when the at least one agricultural machine executes a task of the at least an agricultural machine.

[0709] M87. The method according to any of the preceding method embodiments, wherein the at least one operational attribute comprises a location of a task of the at least one agricultural machine.

[0710] M88. The method according to any of the preceding method embodiments, with the features of embodiments M50 and M87, wherein the method comprises determining the location of a task of the at least one agricultural machine based at least in part on data related to the location of the at least one agricultural machine. M89. The method according to any of the preceding method embodiments, wherein the at least one operational attribute comprises a starting and / or ending and / or pausing time of a task of the at least one agricultural machine.

[0711] M90. The method according to any of the preceding method embodiments, with the features of embodiments M51 and M89, wherein the method comprises determining the starting and / or ending and / or pausing time of a task of the at least one agricultural machine based at least in part on data related to the time of work of the at least one agricultural machine.

[0712] M91. The method according to any of the preceding method embodiments, with the features of embodiment M87, wherein the method comprises using an agricultural zone as the location of a task of the at least one agricultural machine.

[0713] M92. The method according to any of the preceding method embodiments, with the features of embodiment M87, wherein the method comprises using an agricultural field as the location of a task of the at least one agricultural machine.

[0714] M93. The method according to any of the preceding method embodiments, with the features of embodiment M87, wherein the location of a task of the at least one agricultural machine comprises a position of the at least one agricultural machine, when the at least one agricultural machine executes the task.

[0715] M94. The method according to any of the preceding method embodiments, with the features of embodiment M93, wherein the method comprises determining the position of the at least one agricultural machine based at least in part on data related to the location of the at least one agricultural machine.

[0716] M95. The method according to any of the preceding method embodiments, with the features of embodiment M87, wherein the method comprises comparing data related to the location of the at least one agricultural machine to reference coordinates, wherein said reference coordinates define the perimeter of an agricultural zone.

[0717] M96. The method according to any of the preceding method embodiments, with the features of embodiment M95, wherein the method comprises identifying if the at least one agricultural machine is located within the perimeter of the agricultural zone. M97. The method according to any of the preceding method embodiments, with the features of embodiments M89 and M95, wherein the method comprises identifying the start of a task of the at least one agricultural machine when the at least one agricultural machine switches from being located out of the perimeter of the agricultural zone to being located within the perimeter of the agricultural zone.

[0718] M98. The method according to any of the preceding method embodiments, with the features of embodiments M89 and M95, wherein the method comprises identifying the end and / or the pause of a task of the at least one agricultural machine when the at least one agricultural machine switches from being located within the perimeter of the agricultural zone to being located out of the perimeter of the agricultural zone.

[0719] M99. The method according to any of the preceding method embodiments, with the features of embodiment M87, wherein the method comprises keeping track of the position of the at least one agricultural machine from a first time to a second time based at least in part on data related to the location of the at least one agricultural machine and at least in part on data related to the time of work of the at least one agricultural machine.

[0720] M100. The method according to any of the preceding method embodiments, with the features of embodiment M93, wherein method comprises adding a tolerance radius to the position of the at least one agricultural machine.

[0721] M101. The method according to any of the preceding method embodiments, with the features of embodiment M100, wherein the method comprises setting the tolerance radius based on a detected implement.

[0722] M102. The method according to any of the preceding method embodiments, with the features of embodiment M100, wherein the tolerance radius is smaller or equal to 5 m, preferably smaller or equal to 10 m, more preferably smaller or equal to 30 m.

[0723] M103. The method according to any of the preceding method embodiments, with the features of embodiments M89 and M96 and M99, wherein the method comprise identifying the end of a task of the at least one agricultural machine, when the at least one agricultural machine switches from being located within the perimeter of the agricultural zone to being located out of the perimeter of the agricultural zone, and when the tracked position of the at least one agricultural machine covers more than a threshold portion of the agricultural zone.

[0724] M104. The method according to any of the preceding method embodiments, with the features of embodiment M103, wherein the method comprises identifying the start of a new task of the at least one agricultural machine after the end of a task of the at least one agricultural machine.

[0725] M105. The method according to any of the preceding method embodiments, with the features of embodiments M89 and M95 and M99, wherein the method comprises identifying a pause of a task of the at least one agricultural machine, when the at least one agricultural machine switches from being located within the perimeter of the agricultural zone to being located out of the perimeter of the agricultural zone, and when the tracked position of the at least one agricultural machine covers essentially less than threshold portion of the agricultural zone.

[0726] M106. The method according to any of the preceding method embodiments, with the features of embodiment M105, wherein the method comprises deleting the task and / or mark the task as ended and / or merge the task with another task of the at least one agricultural machine, after having identified the pause of said task.

[0727] M107. The method according to any of the preceding method embodiments, with the features of embodiments M23 and M105, wherein the method comprises deleting the task and / or marking the task as ended and / or merging the task with another task of the at least one agricultural machine, after having identified the pause said task, based at least in part on an input from the interface module.

[0728] M108. The method according to any of the preceding method embodiments, with the features of embodiments M103 or M105, wherein the method comprises setting the threshold portion of the agricultural zone amount to 60% of the area of the agricultural zone, preferably to 80% of the area of the agricultural zone, more preferably to 95% of the area of the agricultural zone.

[0729] M109. The method according to any of the preceding method embodiments, with the features of embodiments M24 and M103 or M105, wherein the method comprises using the artificial intelligence module to determine the threshold portion based at least in part on the training of the artificial intelligence. MHO. The method according to any of the preceding method embodiments, with the features of embodiments M103 or M105, wherein the method comprises updating the threshold portion.

[0730] Mill. The method according to any of the preceding method embodiments, with the features of embodiments M23 and M103 or M105, wherein the method comprises updating the threshold portion based at least in part on an input coming from the interface module.

[0731] M112. The method according to any of the preceding method embodiments, with the features of embodiment M105, wherein the method comprises determining an end of the task of the at least one agricultural machine after having identified the pause of said task, if the state of pause of said task persists for more than a threshold of time.

[0732] M113. The method according to any of the preceding method embodiments, with the features of embodiment M105, wherein the method comprises determining an end of the task of the at least one agricultural machine on an agricultural zone after having identified the pause of said task, when said task is taken over by another agricultural machine on the same agricultural zone.

[0733] Ml 14. The method according to any of the preceding method embodiments, with the features of embodiment M105, wherein the method comprises identifying a start of a continuation of the task of the at least one agricultural machine after having identified the pause of the task of the at least one agricultural machine, when the at least one agricultural machine switches from being located out of the perimeter of the agricultural zone to being located within the perimeter of the agricultural zone, wherein said switching happens after the analyzing module identifies the pause of the task of the at least one agricultural machine.

[0734] M115. The method according to any of the preceding method embodiments, with the features of embodiment Ml 14, wherein the method comprises determining an end of the task of the at least one agricultural machine after having identified the continuation of said task, when another task is started by the at least one agricultural machine on the agricultural zone.

[0735] M116. The method according to any of the preceding method embodiments, with the features of embodiments M32 and M89, wherein the method comprises determining the start and / or pause and / or end of a task of the at least one agricultural machine based on an input to the analyzing module from the interface module.

[0736] Ml 17. The method according to any of the preceding method embodiments, wherein the at least one operational attribute comprises at least a type of a task of the at least one agricultural machine.

[0737] M118. The method according to any of the preceding method embodiments, with the features of embodiments M55 and M117, wherein the method comprises determining at least a type of a task of the at least one agricultural machine based at least in part on data related to the average speed of the at least one agricultural machine.

[0738] M119. The method according to any of the preceding method embodiments, with the features of embodiments M55 and M117, the method comprises determining at least a type of a task of the at least one agricultural machine based at least in part on data related to the location and / or to the time of work of the at least one agricultural machine.

[0739] M120. The method according to any of the preceding method embodiments, with the features of embodiment M55, wherein the method comprises relating data related to the average speed of the at least one agricultural machine to at least an implement used by the at least one agricultural machine.

[0740] M121. The method according to any of the preceding method embodiments, with the features of embodiments M56 and M117, wherein the method comprises determining at least a type of a task of the at least one agricultural machine based at least in part on data related to the work range of the at least one agricultural machine.

[0741] M122. The method according to any of the preceding method embodiments, with the features of embodiment M56, wherein the method comprises relating data related to the work range of the at least one agricultural machine to at least an implement used by the at least one agricultural machine.

[0742] M123. The method according to any of the preceding method embodiments, with the features of embodiments M57 and M117, wherein the method comprises determining at least a type of a task of the at least one agricultural machine based at least in part on data related to the execution period of the work of the at least one agricultural machine.

[0743] M124. The method according to any of the preceding method embodiments, with the features of embodiment M57, wherein the method comprises relating data related to the execution period of the work of the at least one agricultural machine to at least an implement used by the at least one agricultural machine.

[0744] M125. The method according to any of the preceding method embodiments, with the features of embodiments M64 and M117, wherein the method comprises determining at least a type of a task of the at least one agricultural machine based at least in part on data related to the weather conditions when the at least one agricultural machine works.

[0745] M126. The method according to any of the preceding method embodiments, with the features of embodiment M64, wherein the method comprises relating data related to the weather conditions when the at least one agricultural machine works to at least an implement used by the at least one agricultural machine.

[0746] M127. The method according to any of the preceding method embodiments, wherein the method comprises determining at least a type of a task of the at least one agricultural machine based at least in part on the implement that the at least one agricultural machine uses.

[0747] M128. The method according to any of the preceding method embodiments, with the features of embodiments M55 and M117, wherein the method comprises determining the at least one type of task of the at least one agricultural machine based on at least a logical relation between the average speed of the at least one agricultural machine and the at least one type of task of the at least one agricultural machine.

[0748] M129. The method according to any of the preceding method embodiments, with the features of embodiment M117, wherein the method comprises assigning a range of average speed of the at least one agricultural machine to the at least one type of task that the at least one agricultural machine executes at said average speed.

[0749] M130. The method according to any of the preceding method embodiments, with the features of embodiments M55 and M129, wherein the method comprises comparing the data related to the average speed of the at least one agricultural machine to the range of average speed assigned to the at least one type task of the at least one agricultural machine.

[0750] M131. The method according to any of the preceding method embodiments, with the features of embodiment M130, wherein the method comprises comparing the at least one of type task of the at least one agricultural machine if the data related to the average speed of the at least one agricultural machine is compatible with the range of average speed assigned to the at least one type task of the at least one agricultural machine.

[0751] M132. The method according to any of the preceding method embodiments, with the features of embodiments M56 and M117, wherein the method comprises determining the at least one type of task of the at least one agricultural machine based on at least a logical relation between the work range of the at least one agricultural machine and the at least one type of task of the at least one agricultural machine.

[0752] M133. The method according to any of the preceding method embodiments, with the features of embodiment Ml 17, wherein the method comprises assigning a span of work range of the at least one agricultural machine to the at least one type of task that the at least one agricultural machine executes at said average speed.

[0753] M134. The method according to any of the preceding method embodiments, with the features of embodiment M133, wherein the method comprises comparing the data related to the work range of the at least one agricultural machine to the span of work range assigned to the at least one type task of the at least one agricultural machine.

[0754] M135. The method according to any of the preceding method embodiments, with the features of embodiments M56 and M134, wherein the method comprises identifying the at least one type of task of the at least one agricultural machine if the data related to the work range of the at least one agricultural machine is compatible with the span of work range assigned to the at least one type task of the at least one agricultural machine.

[0755] M136. The method according to any of the preceding method embodiments, with the features of embodiments M56 and M117, wherein the method comprises identifying the at least one type of task of the at least one agricultural machine based on at least a logical relation between the execution period of the work of the at least one agricultural machine and the at least one type of task of the at least one agricultural machine.

[0756] M137. The method according to any of the preceding method embodiments, with the features of embodiment M117, wherein the method comprises assigning a range of execution period of the work of the at least one agricultural machine to the at least one type of task that the at least one agricultural machine executes at said average speed.

[0757] M138. The system according to any of the preceding embodiments, with the features of embodiments M57 and M137, wherein the method comprises comparing the data related to the execution period of the work of the at least one agricultural machine to the range of execution period of the work assigned to the at least one type task of the at least one agricultural machine.

[0758] M139. The method according to any of the preceding method embodiments, with the features of embodiment M138, wherein the method comprises identifying the at least one type of task of the at least one agricultural machine if the data related to the range of execution period of the at least one agricultural machine is compatible with range of execution period of the work assigned to the at least one type task of the at least one agricultural machine.

[0759] M140. The method according to any of the preceding method embodiments, with the features of embodiments M64 and M117, wherein the method comprises identifying the at least one type of task of the at least one agricultural machine based on at least a logical relation between the weather conditions when the at least one agricultural machine works and the at least one type of task of the at least one agricultural machine.

[0760] M141. The method according to any of the preceding method embodiments, with the features of embodiment M117, wherein the method comprises assigning a range of weather conditions of the at least one agricultural machine to the at least one type of task that the at least one agricultural machine executes at said average speed.

[0761] M142. The method according to any of the preceding method embodiments, with the features of embodiments M64 and M141, wherein the method comprises comparing the data related to the weather conditions when the at least one agricultural machine works to the range of weather conditions assigned to the at least one type task of the at least one agricultural machine.

[0762] M143. The method according to any of the preceding method embodiments, with the features of embodiment M142, wherein the method comprises identifying the at least one type of task of the at least one agricultural machine if the data related to the weather conditions when the at least one agricultural machine works is compatible with range weather conditions assigned to the at least one type task of the at least one agricultural machine.

[0763] M144. The method according to any of the preceding method embodiments, the method comprises identifying the at least one type of task of the at least one agricultural machine based on at least a logical relation between at least an implement that the at least one agricultural machine uses and at least a type of a task of the at least one agricultural machine.

[0764] M145. The method according to any of the preceding method embodiments, with the features of embodiment M32, wherein the method comprises determining the ranges, according to embodiments M129 and / or M133 and / or M137 and / or M141, based on an input from the interface module.

[0765] M146. The method according to any of the preceding method embodiments, with the features of embodiment M32, wherein the method comprises determining the at least one logical relation, according to embodiments M128 and / or M132 and / or M136 and / or M140 and / or M144, based on an input from the interface module.

[0766] M147. The method according to any of the preceding method embodiments, with the features of embodiments M25 and / or M26 and / or M33, wherein the method comprises determining, preferably with the artificial intelligence module, the ranges, according to embodiments M129 and / or M133 and / or M137 and / or M141, based on the training of the artificial intelligence.

[0767] M148. The method according to any of the preceding method embodiments, with the features of embodiments M25 and / or M26 and / or M33, wherein the method comprises determining, preferably with the artificial intelligence module, the at least one logical relation, according to embodiment M128 and / or M132 and / or M136 and / or M140 and / or M144, based on the training of the artificial intelligence. M149. The method according to any of the preceding method embodiments, with the features of embodiments M69 and M117, wherein the method comprises determining the at least one type of a task of the at least one agricultural machine based at least in part on data related to the image of at least a part of the agricultural zone the at least one agricultural machine works on.

[0768] M150. The method according to any of the preceding method embodiments, with the features of embodiments M69 and M117 and M25 and / or M26 and / or M33, wherein the method comprises determining, preferably with the artificial intelligence module, the at least one type of a task of the at least one agricultural machine based at least in part on data related to the image of at least a part of the agricultural zone the at least one agricultural machine works on, and / or based on the training of the artificial intelligence.

[0769] M151. The method according to any of the preceding method embodiments, with the features of embodiments M69 and M25 and / or M26 and / or M33, wherein the method comprises determining, preferably with the artificial intelligence module, at least a crop type and / or cover crop and / or cover crop type and / or crop existence and / or cover crop existence and / or cover crop biomass and / or plant biomass and / or yield and / or an implement and / or a crop yield and / or the like based at least in part on data related to the image of at least a part of the agricultural zone the at least one agricultural machine works on, and / or based on the training of the artificial intelligence.

[0770] M152. The method according to any of the preceding method embodiments, with the features of embodiments M83 and M117, wherein the method comprises determining at least a type of a task of the at least one agricultural machine based at least in part on at least a type of implement used by the at least one agricultural machine, wherein the at least one type of implement is recognized by means of the Bluetooth connection between the at least one Bluetooth device mounted on the at least one agricultural machine and the at least one Bluetooth device mounted on at least one implement used by the at least one agricultural machine.

[0771] M153. The method according to any of the preceding method embodiments, wherein the at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine comprises a report on the utilization of an agricultural zone used by the at least one agricultural machine. M154. The method according to any of the preceding method embodiments, with the features of embodiment M153, wherein the report is adapted for governmental reporting.

[0772] M155. The method according to any of the preceding method embodiments, with the features of embodiment M153, wherein the report is adapted for insurance reporting.

[0773] M156. The method according to any of the preceding method embodiments, with the features of embodiment M153, wherein the report is adapted for sustainable loan reporting.

[0774] M157. The method according to any of the preceding method embodiments, with the features of embodiment M153, wherein the report is adapted for soil organic carbon reporting and / or greenhouse gas emission reporting and / or carbon program reporting.

[0775] M158. The method according to any of the preceding method embodiments, with the features of embodiment M153, wherein the report comprises fields.

[0776] M159. The method according to any of the preceding method embodiments, wherein the method comprises analyzing, preferably with the artificial intelligence module, the at least one the at least one data indicative of the utilization of at least an agricultural zone according to a standardized database.

[0777] M160. The method according to any of the preceding method embodiments, with the features of embodiment M158, wherein said fields relate at least in part to the location of a task of the at least one agricultural machine.

[0778] M161. The method according to any of the preceding method embodiments, with the features of embodiment M158, wherein said fields relate at least in part to the starting and / or ending and / or pausing time of a task of the at least one agricultural machine.

[0779] M162. The method according to any of the preceding method embodiments, with the features of embodiment M158, wherein said fields relate at least in part to the type of a task of the at least one agricultural machine. M163. The method according to any of the preceding method embodiments, wherein the at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine comprises an estimation of a change in greenhouse gas emission related at least in part to the at least one agricultural zone.

[0780] M164. The method according to any of the preceding method embodiments, wherein the at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine comprises an estimation of a greenhouse gas emission related at least in part to the at least one agricultural zone.

[0781] M165. The method according to any of the preceding method embodiments, wherein the at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine comprises an estimation of a greenhouse gas emission related at least in part to the at least one agricultural zone.

[0782] M166. The method according to any of the preceding method embodiments, wherein the at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine comprises an estimation of a balance of soil organic carbon related at least in part to the at least one agricultural zone.

[0783] M167. The method according to any of the preceding method embodiments, wherein the at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine comprises an estimation of a change of soil organic carbon related at least in part to the at least one agricultural zone.

[0784] M168. The method according to any of the preceding method embodiments, wherein the at least one data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine comprises an estimation of carbon sequestration related at least in part to the at least one agricultural zone.

[0785] M169. The method according to any of the preceding method embodiments, wherein the method comprises generating a soil organic carbon model and / or a greenhouse gas emission model and / or a carbon sequestration model related to at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0786] M170. The method according to any of the preceding method embodiments, with the features of any of embodiments M35, M36, M37, and M38, wherein the method no

[0787] comprises generating, preferably with the modelling module, a soil organic carbon model and / or a greenhouse gas emission model and / or a carbon sequestration model related to at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0788] M171. The method according to any of the preceding method embodiments, with the features of any of embodiments M169 and M170, wherein the method comprises training at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine based on said model.

[0789] M172. The method according to any of the preceding method embodiments, wherein the method is a method for optimizing the utilization of at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0790] M173. The method according to any of the preceding method embodiments, wherein the method is a method for generating at least a report, said report concerning at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0791] M174. The method according to any of the preceding method embodiments, wherein the method is a method for calculating soil organic carbon change, said change concerning at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0792] M175. The method according to any of the preceding method embodiments, wherein the method is a method for calculating and / or modelling greenhouse gas emissions, said greenhouse gas emissions concerning at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0793] M176. The method according to any of the preceding method embodiments, wherein the method is a method for optimizing carbon sequestration, said carbon sequestration concerning at least an agricultural zone and / or the usage of said agricultural zone by at least an agricultural machine.

[0794] M177. The method according to any of the preceding method embodiments, wherein the method comprises training and / or optimizing the at least one agricultural zone and / or the usage of said agricultural zone by the at least one agricultural machine based on the at least one operational attribute. Ill

[0795] M178. The method according to any of the preceding method embodiments, with the features of M177, wherein, in said training and / or optimization, the method comprises outputting at least a suggestion on the use of the at least one agricultural machine such as to reduce the difference between the at least one data indicative of the utilization of the at least one agricultural zone and at least a standardized value and / or a threshold value.

[0796] M179. The method according to any of the preceding method embodiments, with the features of M178 and M23, wherein the method comprises receiving from an authorized user said threshold value.

[0797] M180. The method according to any of the preceding method embodiments, with the features of M178 and M24, wherein the method comprises determining said suggestion based at least in part on the training of the artificial intelligence module.

[0798] M181. The method according to any of the preceding method embodiments, with the features of M23, wherein the method comprises receiving user training data for the artificial intelligence module from an authorized user.

[0799] M182. The method according to any of the preceding method embodiments, with the features of M24 and M181, wherein the method comprises training the artificial intelligence module on said user training data, wherein said user training data comprise at least an image of at least an implement that the at least one agricultural machine may use and the type of task of the at least one agricultural machine corresponding to said implement.

[0800] M183. The method according to any of the preceding method embodiments, with the features of M25 and M181, wherein the method comprises training the artificial intelligence module on the data related to at least an agricultural machine, preferably image data of at least an implement that the at least one agricultural machine may use, and on said user training data, wherein said user training data comprises the type of said implement and / or the type of task of the at least one agricultural machine corresponding to said implement.

[0801] M184. The method according to any of the preceding method embodiments, with the features of M25, wherein the method comprises determining a type of task of the at least one agricultural machine and / or the type of implement used by the at least one agricultural machine before the at least one agricultural machine enters an agricultural field and / or an agricultural zone.

[0802] M185. The method according to any of the preceding method embodiments, with the features of M23, M67, and M68, wherein the method comprises recognizing and censoring faces in the data acquired by the at least one image recording device and / or by the at least one camera.

[0803] M186. The method according to any of the preceding method embodiments, with the features of M23, M67, and M68, wherein the method comprises recognizing faces in the image data acquired by the at least one image recording device and / or by the at least one camera and delete said data.

[0804] M187. The method according to any of the preceding method embodiments, with the features of M23, M67, and M68, wherein the method comprises recognizing if the image data acquired by the at least one image recording device and / or by the at least one camera does not relate, at least in part, to an agricultural zone and / or to an agricultural field, and delete said data.

[0805] M188. The method according to any of the preceding method embodiments, with the features of M24, M67, and M68, wherein method comprises determining, based on data acquired by the at least one image recording device and / or by the at least one camera, the type of product that is loaded on an implement that the at least one agricultural machine uses, when the product is being loaded and / or when the at least one agricultural machine uses the implement with the product.

[0806] M189. The method according to any of the preceding method embodiments, with the features of M24, wherein the method comprises sending an intervention prompt to an authorized user, preferably via the interface module.

[0807] M190. The method according to any of the preceding method embodiments, with the features of M189, wherein, in the intervention prompt, the method comprises outputting at least a request of input to an authorized user, the input relating to the current and / or future use of an implement of the at least one agricultural machine.

[0808] M191. The method according to any of the preceding method embodiments, with the features of M189, wherein, in the intervention prompt, the method comprises outputting at least a request of intervention on the sensor module to an authorized user.

[0809] M192. The method according to any of the preceding method embodiments, with the features of M67 and M68, wherein the at least on image recording device and / or the at least one camera is secured to a transparent panel of the at least one agricultural machine.

[0810] M193. The method according to any of the preceding method embodiments, with the features of M67 and M68, wherein the at least on image recording device and / or the at least one camera is powered via a battery present on the at least one agricultural machine.

[0811] M194. The method according to any of the preceding method embodiments, with the features of M67 and M68, wherein the at least on image recording device and / or the at least one camera is powered via a 3-pin DIN connector.

[0812] M195. The method according to any of the preceding method embodiments, with the features of M67 and M68, wherein the at least on image recording device and / or the at least one camera is powered via a power socket present in the cabin of the at least one agricultural machine.

[0813] M196. The method according to any of the preceding method embodiments, with the features of M24, wherein the method comprises determining and / or updating the at least one operational attribute at least when the at least one agricultural machine undergoes a task changing event.

[0814] M197. The method according to any of the preceding method embodiments, with the features of M24, wherein the method comprises determining a type of task of the at least one agricultural machine and / or the type of implement used by the at least one agricultural machine when the at least one agricultural machine undergoes a task changing event.

[0815] M198. The method according to any of the preceding method embodiments, with the features of M196, wherein the task changing event comprises the at least one of: the at least one agricultural machine leaving a task changing zone, the at least one agricultural machine coming to a halt, the at least one agricultural machine turning off, the at least one agricultural machine entering and / or leaving the agricultural zone and / or agricultural area. M199. The method according to any of the preceding method embodiments, with the features of M149, wherein, before determining the at least one type of task based on the data related to the image of at least a part of the agricultural zone, the method comprises iteratively determining the quality the data related to the image and prompt the sensor module to acquire new data until the determined quality is above a quality threshold.

[0816] M200. The method according to any of the preceding method embodiments, with the features of M14, wherein the storing module is, at least in part, remotely arranged and, at least in part, locally arranged on the at least one agricultural machine and wherein the system is configured to operate independently of the remotely arranged part of the storing module.

[0817] M201. The method according to any of the preceding method embodiments, with the features of M45 and / or M46 and / or M47 and / or M48 and / or M49 and / or M61 and / or M62, wherein the method comprises acquiring data utilizing at least one among the position sensor, the time sensor, the positioning system, the satellite navigation system, the GPS navigation system, the weather sensor, the internet connection at a frequency between 0.5 seconds and 5 minutes, preferably between 1 second and 1 minute, more preferably between 3 seconds and 7 seconds.

[0818] M202. The method according to any of the preceding method embodiments, with the features of M67 and / or M68, wherein the method comprises acquiring data utilizing at least one among the image recording device, the camera at an acquisition frequency between 30 seconds and 15 minutes, preferably between 1 minute and 10 minutes, more preferably between 3 minutes and 7 minutes.

[0819] M203. The method according to any of the preceding method embodiments, with the features of M45 and / or M46 and / or M47 and / or M48 and / or M49 and / or M61 and / or M62, wherein the method comprises acquiring data utilizing at least one among the position sensor, the time sensor, the positioning system, the satellite navigation system, the GPS navigation system, the weather sensor, the internet connection upon a trigger signal.

[0820] M204. The method according to any of the preceding method embodiments, with the features of M67 and / or M68, wherein the method comprises acquiring data utilizing at least one among the image recording device, the camera upon a trigger signal. M205. The method according to any of the preceding method embodiments, with the features of M203 and / or M204, wherein the trigger signal is received, preferably by the interface module, from an authorized user.

[0821] M206. The method according to any of the preceding method embodiments, with the features of M203 and / or M204, wherein the trigger signal is remotely received, preferably by the interface module, from an authorized user.

[0822] M207. The method according to any of the preceding method embodiments, with the features of M198 and M203 and / or M204, wherein the trigger signal is received when the at least one agricultural machine undergoes a task changing event.

[0823] M208. The method according to any of the preceding method embodiments, with the features of M203 and / or M204, wherein the trigger signal relates to one or more events and / or conditions in the operation of the at least one agricultural machine.

[0824] M209. The method according to any of the preceding method embodiments, with the features of M23 and M201 and / or M202, wherein the method comprises allowing an authorized user to change said acquisition frequency(ies).

[0825] M210. The method according to any of the preceding method embodiments, with the features of M24, wherein the method comprises sending an input prompt to an authorized user, preferably via the interface module.

[0826] M211. The method according to any of the preceding method embodiments, with the features of M210, wherein, in the input prompt, the method comprises outputting at least a request of input to an authorized user, the input relating to the current and / or future use of an implement of the at least one agricultural machine and / or to the use of the at least one agricultural machine.

[0827] M212. The method according to any of the preceding method embodiments, with the features of M210, wherein the request of input is sent upon a triggering event.

[0828] M213. The method according to any of the preceding method embodiments, with the features of M212, wherein the triggering event relates to a change in the activity of the at least one agricultural machine. M214. The method according to any of the preceding method embodiments, with the features of M210, wherein the input prompt is a call carried out by the analyzing module, preferably by the Al module.

[0829] M215. The method according to any of the preceding method embodiments, with the features of M24, wherein method comprises running an Al agent.

[0830] M216. The method according to any of the preceding method embodiments, with the features of M212 and M213, wherein the call is run by the Al agent.

[0831] M217. The method according to any of the preceding method embodiments, with the features of M210 and / or M24, wherein the method comprises storing the input(s), preferably in the storing module.

[0832] M218. The method according to any of the preceding method embodiments, with the features of M210 and M24, wherein the method comprises training the Al module on the input(s) stored.

[0833] M219. The method according to any of the preceding method embodiments, with the features of M24, wherein method comprises providing the Al module with the data related to the at least one agricultural machine and the Al module building contextual awareness based thereon.

[0834] M220. The method according to any of the preceding method embodiments, with the features of M24, wherein the method comprises sending an alert based on an alert threshold.

[0835] M221. The method according to any of the preceding method embodiments, with the features of M218, wherein the alert threshold relates to a standardized use of the at least one agricultural machine and / or the agricultural field that the at least one agricultural machine works on.

[0836] M222. The method according to any of the preceding method embodiments, with the features of M23 and M218, wherein the method comprises receiving from an authorized user said alert threshold.

[0837] M223. The method according to any of the preceding method embodiments, with the features of M23 and M24, wherein the method comprises determining with the Al module said alert threshold. M224. The method according to any of the preceding method embodiments, wherein the method comprises sending the data related to at least an agricultural machine to an external Al model for training the external Al model.

[0838] M225. The method according to any of the preceding method embodiments, wherein the method comprises carrying out the method according to any of the preceding method embodiments via the system according to any of the preceding system embodiments.

[0839] 5225. The system according to any of the preceding system embodiments, wherein the system is adapted to carry out the method recited in any of the preceding method embodiments.

[0840] 5226. The system according to any of the preceding system embodiments, wherein the system is adapted to carry out any given step of the method recited in any of the preceding method embodiments.

[0841] Below, use embodiments are presented. Use embodiments are abbreviated by the letter "U" followed by a number. Whenever reference is made herein to "use embodiments", these embodiments are meant.

[0842] Ul. Use of the system according to any of the preceding system embodiments.

[0843] U2. Use according to the preceding embodiment for carrying out the method according to any of the preceding method embodiments.

[0844] Below, computer program embodiments are presented. Computer program embodiments are abbreviated by the letter "C" followed by a number. Whenever reference is made herein to "computer program embodiments", these embodiments are meant.

[0845] Cl. A computer program comprising instructions which, when executed by a processing component, cause the component to carry out the method according to any of the preceding method embodiments.

[0846] C2. A computer program product comprising instructions which, when executed by a processing component, cause the component to carry out the method according to any of the preceding method embodiments. C3. A computer-readable medium comprising instructions which, when executed by a processor, cause the computer to carry out the method according to any of the preceding method embodiments.

[0847] C4. A data carried signal carrying the computer program of embodiment Cl.

[0848] C5. A data carried signal carrying the computer program product of embodiment C2.

[0849] Brief description of the figures

[0850] Fig. 1 depicts, as an example, a high-level overview of a method for automatic agricultural data collection according to a preferred embodiment of the present invention.

[0851] Fig. 2 depicts, as an example, an overview of a system for automatic agricultural data collection according to a preferred embodiment of the present invention.

[0852] Fig. 3 depicts, as an example, a tractor equipped with a plurality of sensor devices and communication devices according to a preferred embodiment of the present invention.

[0853] Fig. 4A, 4B depicts, as an example, steps of the determination of the location of a task of an agricultural machine and the timing of a task of an agricultural machine, according to a preferred embodiment of the present invention.

[0854] Fig. 5A to 5E depicts, as an example, steps of the determination of the type of task of an agricultural machine, according to preferred embodiments of the present invention.

[0855] Detailed description of the figures

[0856] Hereafter, exemplary embodiments of the present invention will be described in detail, referring to the accompanying figures.

[0857] Fig. 1 depicts, as an example, a high-level overview of a method according to a preferred embodiment of the present invention. The method can be a method for automatic agricultural data collection. The method can comprise the step of acquiring data 1 related to one or more agricultural machines 2. The method can comprise acquiring data 1 related to one or more agricultural machines 2 with of a variety of sensors.

[0858] The term sensor is intended to comprise at least one device, module, model, and / or subsystem whose purpose is to detect parameters and / or changes in its environment and provides a respective signal to other devices. It will be understood that the term "variety of sensors" or the like is intended to mean sensors that are configured to measure different parameters or the same parameters with different technologies.

[0859] The method can further comprise the step of analyzing 3 the data related to the one or more agricultural machines. More in particular, the method can comprise determining one or more operational attributes of the one or more agricultural machine based on the acquired data. It will be understood that an operational attribute of an agricultural machine can comprise a characteristic or property that can contribute to the definition of a function performed by the agricultural machine. For example, an operational attribute can comprise, but not being limited to, the location where the agricultural machine works, i.e. the place of a task that the agricultural machine executes, and / or the time when the agricultural machine works, i.e. the time of a task that the agricultural machine executes, and / or the type of task that the agricultural machine executes. The step of analyzing may comprise the utilization of an AL

[0860] The method can further comprise the step of processing 4 the one or more operational attributes. More in particular, the method can comprise generating one or more data indicative of the utilization of the one or more agricultural zones by the one or more agricultural machines based on the one or more operational attributes. An agricultural zone can comprise, among others, a field where the one or more agricultural machines work. The one or more data indicative of the utilization of one or more agricultural zones can comprise, for instance and without limitation, a report, and / or an estimation of a greenhouse gas emission or a change thereof, and / or a balance of soil organic carbon or a change thereof, and / or an estimation of carbon sequestration or change thereof.

[0861] The one or more data indicative of the utilization of one or more agricultural zones may be useful in at least the following contexts.

[0862] Many well-established soil and greenhouse gas emission models may need, for example, a precise overview of tasks executed on the field to calculate, for instance, a change in soil organic carbon change or in greenhouse gas emissions. The one or more data indicative of the utilization of one or more agricultural zones may provide said precise overview. Furthermore, more and more stakeholders, such as food companies, banks, et cetera, may ask for greenhouse gas emission overviews and / or models from their clients, which can be based on the one or more data indicative of the utilization of one or more agricultural zones. An example of soil model that may benefits from task data can be, without limitation, RothC, DNDC, CoolFarmTool, Armosa.

[0863] Farmers may further join a carbon program where they get paid for additional carbon sequestration in the soil and / or greenhouse gas emission reductions compared to their previous practices. These programs often rely on soil and greenhouse gas emission models. Farmers in given regions may further need to prepare reports for government inspectors to review.

[0864] Overall, there may be an increasing need for farmers to report to different stakeholders and to get better overviews themselves.

[0865] Fig. 2 depicts, as an example, an overview of a system according to a preferred embodiment of the present invention. The system can be a system for automatic agricultural data collection.

[0866] The system can comprise a sensor module 5, which can in turn comprise a variety of sensors and can be configured to acquire data related to one or more agricultural machines 2. The sensor module may be installed on the one or more agricultural machine. It will be understood that an agricultural machine can comprise a machine, device, and / or equipment designed or used for agricultural practices. To name some non-limiting examples, an agricultural machine can comprise a tractor, a harvesting combine, a plow, a seeder, a planter, an irrigation system, a baler, a self-propelled sprayer, and / or a selfdriving sprayer. The sensor module 5 may comprise a plurality of sensors, a sensor system or a plurality of sensor systems. The sensor module may therefore be referred to as a plurality of sensors, a plurality of sensor systems, a sensor system, sensors or simply as a sensor.

[0867] The system can comprise an analyzing module 6, which can be configured to receive the data related to one or more agricultural machines. The analyzing module 6 can further be configured to determine one or more operational attributes of the one or more agricultural machines, based on the data related to the one or more agricultural machine.

[0868] The system can further comprise a processing module 7, which can be adapted to generate one or more data indicative of the utilization of one or more agricultural zones by the one or more agricultural machines based on the one or more operational attributes. The system can further comprise a modelling module 8. The modelling module may receive the one or more data indicative of the utilization of the one or more agricultural zones. The modelling module may further be adapted to generate, for example and without limitation, a soil organic carbon model and / or a greenhouse gas emission model and / or a carbon sequestration model related the one or more agricultural zones and / or the usage of said agricultural zones the one or more agricultural machines.

[0869] The model and / or the one or more data indicative of the utilization of the one or more agricultural zones may be used to train the one or more agricultural zones the one or more agricultural machines work on. This is represented by the dashed arrow in Fig. 2. In other words, the system may provide a means for the improvement the utilization of the one or more agricultural zones the one or more agricultural machines work on. The improvement may be an improvement of the environmental sustainability of the one or more agricultural zones and / or of the work performed on the one or more agricultural zones. For example, the improvement may be a reduction of greenhouse gas emission from the one or more agricultural zones.

[0870] The system can comprise a communication module 9, which may be directly installed on the one or more agricultural machines. The system may further comprise a server 10, which may be a remote server. The communication module 9 may be, for example, connected to the internet and may be configured to transmit the data related to the one or more agricultural machines to another module comprised in the system, such as to the server 10. The server can be configured to bidirectionally communicate with a storing module 11. The storing module can comprise a memory device, configured to store data. For example, the storing module 11 can store the data related to the one or more agricultural machines and / or an output of the analyzing module 6 and / or and output of the processing module 7. The server 10 may also be adapted to bidirectionally communicate with the analyzing module 6 and with the processing module 7.

[0871] In one embodiment, the sensor module can comprise a GPS device, that may be installed on the tractor and may be connected to the internet via the communication module. The communication module can be adapted to send the data from the GPS device to the server 10, where it may be saved in the storing module 11 for further analysis.

[0872] The analyzing module can comprise an Al module 12, which can be trained, without limitation, according to one or more of the following training possibilities. The Al module can be trained on the data related to at least an agricultural machine. The Al module can be trained on the one or more operational attributes. The Al module can be trained on data stored in the storing module 11.

[0873] The system can comprise an interface module 13, which may be configured to provide, to an authorized user, access to any of the modules of the system and to communicate with any of the modules of the system. Further, the interface module 13 may be configured to change a parameter related to any modules of the system. For example, a user may want to change parameters of the acquisition of data related to the one or more agricultural machines, for example the parameters used by a GPS system comprised in the sensor module 5, via the interface module. As another example, a logic used in the determination of one or more operational attributes, such as the type of task executed by the one or more agricultural machines, may be changed via the interface module. Moreover, the Al module 12 may be trained based on inputs to the interface module, such as the input of a user, which may be a farmer.

[0874] It will be understood that the interface module 13 may comprise a plurality of software interfaces with different levels. In one embodiment, the interface module 13 may also comprise a physical terminal for providing access to the server 10 to an authorized user, or to any other module of the system. Furthermore, the interface module 13 may be configured to facilitate providing instructions to any module of the system and / or for requesting information from any module of the system, such as, for example, historic data saved in the storing module 11.

[0875] Fig. 3 depicts, as an example, a tractor 14 equipped with a plurality of sensor devices and communication devices according to a preferred embodiment of the present invention.

[0876] It will be understood that the position of the plurality of sensor devices in the embodiment of Fig. 3 may be accidental.

[0877] The tractor may be equipped with a plurality of implements. According to the embodiment of Fig. 3, the tractor 14 may be equipped with a first implement 15 and with a second implement 16.

[0878] The plurality of sensor devices can comprise a satellite navigation system 17, such as, without limitation, GLONASS, COMPASS, EGNOS, GALILEO, and / or GPS, configured to communicate to one or more satellites 18. Said satellite navigation system 17 can provide data related to, e.g., the location of the tractor and / or the time when the tractor is at work. Said satellite navigation system 17 can provide data related to, e.g., the execution period of the work of the one or more agricultural machine, such as the season of the year and / or the month of the year during which the machine works. Said satellite navigation system can also provide data related to, e.g., the weather conditions and / or weather parameter, such as temperature and / or amount of rain.

[0879] Additionally, or alternatively, the plurality of sensor devices can comprise can comprise a temperature sensor 19 and / or a weather sensor 20.

[0880] Furthermore, the tractor 14 may be equipped with one or more image recording devices 21, for instance, a camera. The image recording device 21 can be inside or outside of the tractor 14. The image recording device may be configured to acquire an image data of, for example, the field area that the agricultural machine works on, or of an implement that the agricultural machine is equipped with.

[0881] The plurality of sensor devices can further comprise implement recognizing devices 22, 22', 22". These can for example be Bluetooth devices. A Bluetooth device 22 may be installed on the tractor. Bluetooth devices 22', 22" may be installed on each implement used by the tractor 14 and may be connected to the Bluetooth device 22. Such Bluetooth devices 22', 22" may be used to recognize the type of implement used by the tractor 14. One or more sensors or devices comprised in the plurality of sensor devices may be configured to transmit data to the communication module 9, which in turn can be configured to communicate with further modules.

[0882] Figs. 4A and 4B depict, as an example, steps of the determination of the location of a task of an agricultural machine 23 and the timing of a task of an agricultural machine 23, according to a preferred embodiment of the present invention.

[0883] The data used in the step of the determination of the location of a task of an agricultural machine 23 and the timing of a task of an agricultural machine 23 may be data the related to the location of an agricultural machine and data related to the time of work of an agricultural machine. Such data can be acquired utilizing, for example, a GPS system 24.

[0884] The component 25 can comprise, for example, any combination of modules according to, e.g., the embodiments of Fig. 2. For example, the component 25 can comprise the analyzing module and the processing module. The component 25 may therefore make use of an artificial intelligence algorithm.

[0885] The location of a task of an agricultural machine 23 may comprise the field 28 where the task is executed. The timing of a task of an agricultural machine 23 may be the date when the task is finished. According to one embodiment, GPS data related to the location of the agricultural machine 23 can be compared to the border coordinates of the field 28. For instance, if the GPS data related to the location of the agricultural machine 23 location signals that the agricultural machine 23 enters the field 28 (as illustrated by the dotted arrow in Fig. 4A), then the component 25 may identify the start of a task of the agricultural machine 23.

[0886] Further, the GPS data related to the location of the agricultural machine 23 can be tracked over time to identify a trajectory of the agricultural machine 23 (as illustrated by the dotted line in Fig. 4B). The position of the agricultural machine 23 in the immediate vicinity of the trajectory may be defined within a tolerance area (as illustrated by the grey shading surrounding the dotted line in Fig. 4B). If the trajectory of the agricultural machine covers more than a threshold portion of the field 28, then the component 25 may identify the end or the pause of the task of the agricultural machine 23. The end or the pause of the task of the agricultural machine 23 may also be defined manually.

[0887] Further, if GPS data related to the location of the agricultural machine 23 signals that the agricultural machine 23 enters the field 28 after the end or the pause of the task of agricultural machine 23, then the component 25 may identify the start of a new task of agricultural machine 23 or the continuation of the paused task of the agricultural machine 23, respectively.

[0888] Figs. 5A to 5E depict, as an example, steps of the determination the type of task of an agricultural machine 23, according to preferred embodiments of the present invention.

[0889] It will be understood that embodiments of the present invention can be directed to the determination of the type of task of an agricultural machine 23 by analyzing, for example, movement patterns based on GPS data related to the location of the agricultural machine 23 and to the time of work of the agricultural machine 23. The analysis may involve, without limitation, a speed of the agricultural machine 23, the time of the season when of the agricultural machine 23 performs work, the number of passes that the agricultural machine 23 executes on a field 28 and / or how close the passes that the agricultural machine 23 executes on a field 28 are to each other.

[0890] Embodiments of the present invention can also be directed to the determination the type of task of the agricultural machine 23 with additional precision by combining GPS movement patterns with other available information, including, without limitation, information on implements used by the agricultural machine 23 provided, e.g., by a farmer, activities that other farmers perform in an agricultural region comprising the field 28, and / or weather data.

[0891] It will be understood that most types of task that can be performed by an agricultural machine may have usual ranges, for instance, for an average speed, an average work range, a usual execution period within the year, and / or usual weather conditions.

[0892] The component 25 can comprise, for example, any combination of modules according to, e.g., the embodiments of Fig. 2.

[0893] Fig. 5A depicts, as an example, steps for analyzing data related to the average speed of an agricultural machine and for determining the type of task of a first agricultural machine 26 and of a second agricultural machine 26' based thereon.

[0894] Data related to the average speed may be acquired by means of analyzing movement patterns based on GPS data related to the location of the first and second agricultural machines and to the time of work of the of the first and second agricultural machines.

[0895] The first agricultural machine 26 may be equipped with a first implement 27, the second agricultural machine 26' may be equipped with a second implement 27'.

[0896] The average speed of the first agricultural machine 26, equipped with the first implement 27, may be larger than the average speed of the second agricultural machine 26', equipped with the second implement 27'. This is depicted by the dotted arrows in Fig 5A. Therefore, the type of task executed by the first and second agricultural machines can be inferred. For example, ploughing may be executed with a much slower speed compared to other cultivation practices, or compared to drilling.

[0897] Fig. 5B depicts, as an example, steps for analyzing data related to the average work range of an agricultural machine and for determining the type of task of a first agricultural machine 26 and of a second agricultural machine 26' based thereon.

[0898] The average work range of the first agricultural machine 26, equipped with the first implement 27, may be larger than the work range of the second agricultural machine 26', equipped with the second implement 27'. This is depicted by the dotted arrows in Fig 5B. The dotted arrows may depict trajectories and the surrounding shaded areas the work ranges of the first and second agricultural machines. Therefore, the type of task executed by the first and second agricultural machines can be inferred. For example, a sprayer might have a work range of 12 meters, while a fertilizer drill only of 4 meters. The GPS movement patterns, such as the trajectories of the first and second agricultural machines, can be analyzed and the determination of the type of equipment may be used to decide the type of task that happens on the field 28. Farmers may also, via e. g. the component 25, provide data relating to the usual work range of implements width to facilitate detecting the equipment.

[0899] Fig. 5C depicts, as an example, steps for analyzing data related to an execution period of the work of an agricultural machine 26 and / or to weather conditions when the agricultural machine 26 works, and for determining the type of task of the agricultural machine 26 based thereon.

[0900] Data related an execution period of the work of an agricultural machine 26 26 and / or to weather conditions when the agricultural machine 26 works may be acquired based on GPS data, by means of a temperature sensor 19, and / or a weather sensor 20.

[0901] As an example, sowing in Estonia may generally happen from April / mid-May until August / September. Therefore, if the data related to the execution period of the work of an agricultural machine 26 indicates that said execution period falls within a time frame from April / mid-May until August / September in Estonia, then determination of the task type as sowing may be simplified.

[0902] As another example, sowing may be done in Estonia only if the weather is warm enough, e.g. in the Spring / Summer, while fertilizing can also happen while the weather is cooler than in a typical Spring / Summer in Estonia.

[0903] It will be understood that the usual ranges that most types of tasks that can be performed by an agricultural machine may have can be determined in several ways, including, without limitation, the following examples.

[0904] A set-up of the usual ranges may be based on historical data and / or on know-how for a given agricultural region. Ranges for each task type and a logic for determining a task type based, e.g., on average speed and / or work range and / or usual execution period and / or usual weather conditions can be further provided.

[0905] The usual ranges may be improved, for example, based on a specific farmer's input. A farmer input, such as an input about crops that grown and / or specific implements used in a specific agricultural zone comprising agricultural fields, can make usual ranges specific for the farmer. The usual ranges may be improved, for example, based on an Al-solution. In other words, an Al may improve the usual ranges. The training of the Al may be based, at least in part, on data collected manually and / or automatically by several farmers according to embodiments of the present invention. The manual collection may involve farmers overriding GPS-based task type detection.

[0906] Fig. 5D depicts, as an example, steps for analyzing data related to an image of the field 28 and for determining the type of an agricultural machine 26 based thereon.

[0907] Data related to an image of the field 28 be acquired by means of an image recording device 21, for instance, a camera. The camera may be positioned behind or in front of a tractor, inside or outside of a tractor. The camera may be configured to take a picture of the field 28 where the agricultural machine 27 works. The field may contain crops 29. An Al may be utilized to determine the type of task of the agricultural machine from the picture, based at least in part on the training of the AL Further, the Al could detect a crop type, a cover crop, a cover crop type, a cover crop biomass, and / or the like from the picture.

[0908] Embodiments of the present invention can be directed to using a camera to make task detection easier and detect additional information.

[0909] The camera can be useful in detecting on which field the activity happens, particularly when an Al trained to this purpose analyzes the images of the camera, and in detecting the part of the field where the activity is already done. Analogously, the camera may also help us later to detect if new task is starting or previous one is being continued.

[0910] Fig. 5E depicts, as an example, steps for analyzing data related to a type of an implement used by an agricultural machine 26 and for determining the type of task of an agricultural machine 26 based thereon.

[0911] Data related to a type of an implement used by an agricultural machine 26 may be acquired using Bluetooth devices 22 and 22'. Bluetooth device 22 may be installed on the agricultural machine 26. Bluetooth device 22' may be installed on an implement 15 used by the agricultural machine 26.

[0912] The type of the implement 15 used by the agricultural machine 26 may be determined based on a connection between Bluetooth device 22 and Bluetooth device 22'. The type of task of the agricultural machine 26 may be inferred based thereon. While in this specification preferred embodiments of the present invention are described, the person skilled in the art will understand that the preferred embodiments are provided for illustrative purposes only and to render the disclosure of the present invention complete, and should by no means be construed to limit the scope of the present invention, which is defined by the claims.

[0913] Whenever a relative term, such as "about", "substantially", "essentially" or "approximately" is used in this specification, such a term should also be construed to also include the exact term. That is, e.g., "substantially straight" should be construed to also include "(exactly) straight".

[0914] Whenever steps were recited in the above or also in the appended claims, it should be noted that the order in which the steps are recited in this text may be accidental. That is, unless otherwise specified or unless clear to the skilled person, the order in which steps are recited may be accidental. That is, when the present document states, e.g., that a method comprises steps (A) and (B), this does not necessarily mean that step (A) precedes step (B), but it is also possible that step (A) is performed (at least partly) simultaneously with step (B) or that step (B) precedes step (A). Furthermore, when a step (X) is said to precede another step (Z), this does not imply that there is no step between steps (X) and (Z). That is, step (X) preceding step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Yl), ..., followed by step (Z). Corresponding considerations apply when terms like "after" or "before" are used.

Claims

Claims1. A system for automatic agricultural data collection, the system comprising:at least a sensor module configured to acquire data related to at least an agricultural machine;at least an analyzing module configured to receive the data related to at least an agricultural machine and to determine at least an operational attribute of the at least one agricultural machine based on the data related to at least an agricultural machine;at least a processing module configured to generate at least a data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine based on the at least one operational attribute;2. The system according to the preceding claim, wherein the analyzing module comprises an artificial intelligence (Al) module configured to execute at least an artificial intelligence algorithm, wherein the Al module is configured to be trained at least in part on the data related to the at least one agricultural machine, and / or on the at least one operational attribute, and / or on historical data.

3. The system according to claim 2, wherein the system comprises at least an interface module; wherein the interface module is configured to provide, to an authorized user, access to any of the modules according to any of the preceding claims; wherein the artificial intelligence module is configured to be trained at least in part based on at least an input of data to the artificial intelligence module from the interface module.

4. The system according to any of the preceding claims, wherein the sensor module is configured to be installed on the at least one agricultural machine; wherein the sensor module is configured to acquire data utilizing an image recording device.

5. The system according to any of the preceding claims, wherein the data related to the at least one agricultural machine comprises at least one of data related to at least one image of at least a part of the agricultural zone the at least one agricultural machine works on, wherein said at least one image is an image of at least a crop in the agricultural zone and / or an image of a field ground of the agricultural zone and / or an image of an implement used by the at least one agricultural machine; preferably when the at least one agricultural machine executes a task of the at least one agricultural machine.

6. The system according to any of the preceding claims, wherein the at least one operational attribute comprises at least a type of a task of the at least one agricultural machine.

7. The system according to any of the preceding claims, with the features of claim 5 and 6, wherein the artificial intelligence module is configured to determine the at least one type of task of the at least one agricultural machine based at least in part on the data related to the image of at least a part of the agricultural zone the at least one agricultural machine works on.

8. The system according to any of the preceding claims, with the features of claim 6, wherein the analyzing module is configured to identify the at least one type of task of the at least one agricultural machine based on at least a logical relation between at least an implement that the at least one agricultural machine used and at least a type of task of the at least one agricultural machine, and / or between the weather conditions when the at least one agricultural machine works and and at least a type of task of the at least one agricultural machine.

9. The system according to any of the preceding claims, with the features of claim 8, wherein the artificial intelligence module is configured to determine the at least one logical relation based on the training of the artificial intelligence.

10. The system according to any of the preceding claims, with the features of claim 5, wherein the artificial intelligence module is configured to determine a crop type, a cover crop, a cover crop type, a crop existence, a cover crop existence, a cover crop biomass, a plant biomass, an implement, a crop yield, based at least in part on the data related to the image of at least a part of the agricultural zone the at least one agricultural machine works on.

11. The system according to any of the preceding claims, with the features of claims 3 and 5, wherein the interface module is configured to receive user training data for the artificial intelligence module from an authorized user; wherein the artificial intelligence module is configured to be trained on said user training data, wherein said user training data comprise at least an image of at least an implement that the at least one agricultural machine may use and the type of task of the at least one agricultural machine corresponding to said implement; and / or wherein the artificial intelligence module is configured to be trained on the data related to at least an agricultural machine, preferably image data of at least an implement that the at leastone agricultural machine may use, and on said user training data, wherein said user training data comprises the type of said implement and / or the type of task of the at least one agricultural machine corresponding to said implement.

12. The system according to any of the preceding claims, wherein the sensor module is configured to acquire data utilizing at least one of: a positioning system, a weather sensor, an internet connection, a Bluetooth device.

13. The system according to any of the preceding claims, wherein the data related to the at least one agricultural machine comprises at least one of data related to at least one of: a location of the at least one agricultural machine, a time of work of the at least one agricultural machine, a log of location of the at least one agricultural machine, a log of time of work of the at least one agricultural machine, an average speed of the at least one agricultural machine, a work range of the at least one agricultural machine, an execution period of the work of the at least one agricultural machine, weather conditions when the at least one agricultural machine works.

14. The system according to any of the preceding claims, with the features of the claim 6, wherein the analyzing module is configured to determine the at least one type task of the at least one agricultural machine based at least in part on at least one of data related to: the average speed of the at least one agricultural machine, the work range of the at least one agricultural machine, the execution period of the work of the at least one agricultural machine, the weather conditions when the at least one agricultural machine works.

15. The system according to the preceding claim, wherein the analyzing module is configured to determine the at least one type of task of the at least one agricultural machine based on at least a logical relation between the at least one type of task of the at least one agricultural machine and at least one of: the average speed of the at least one agricultural machine, the work range of the at least one agricultural machine, the execution period of the work of the at least one agricultural machine, the weather conditions when the at least one agricultural machine works, at least an implement that the at least one agricultural machine uses; wherein, particularly, the artificial intelligence module is configured to determine the at least one logical relation, based on the training of the artificial intelligence.

16. The system according to any of the preceding claims, wherein the at least one data indicative of the utilization of at least an agricultural zone by the at least oneagricultural machine comprises a report and / or an estimation of at least one of: a change in greenhouse gas emission, a greenhouse gas emission, a balance of soil organic carbon, a change of soil organic carbon, a carbon sequestration; related at least in part to the at least one agricultural zone.

17. The system according to any of the preceding claims, wherein the system is configured to train and / or optimize the at least one agricultural zone and / or the usage of said agricultural zone by the at least one agricultural machine based on the at least one operational attribute.

18. The system according to any of the preceding claims, with the features of claims 3 and 17, wherein, in said training and / or optimization, the system, preferably via the interface module, is configured to output at least a suggestion on the use of the at least one agricultural machine such as to reduce the difference between the at least one data indicative of the utilization of the at least one agricultural zone and at least a standardized value and / or a threshold value; wherein the interface module is configured to receive from an authorized user said threshold value; wherein the artificial intelligence module is configured to determine said suggestion based at least in part on the training of the artificial intelligence module.

19. A method for automatic agricultural data collection, the method comprising:acquiring data related to at least an agricultural machine;receiving the data related to at least an agricultural machine and determining at least an operational attribute of the at least one agricultural machine based on the data related to at least an agricultural machine;generating at least a data indicative of the utilization of at least an agricultural zone by the at least one agricultural machine based on the at least one operational attribute;

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