Computer-implemented method for generating soil property maps of a field - Patents.com

Abstract

A computer-implemented method for generating a soil property map of a field includes the steps of receiving (S1) crop characteristic distribution data for the field, the crop-related parameter being within a predetermined range of the crop characteristic distribution data, determining (S2) equivalent areas having the crop-related parameter within a predetermined range of the crop characteristic distribution data, receiving (S3) soil data relating to the at least one soil parameter for each of the determined equivalent areas, and generating (S4) a soil property map of the field based on the soil data and the equivalent areas.

Description

[Technical field]

[0001] The present disclosure relates to a computer implemented method for generating a soil property map of a field, a system and computer program elements for generating a soil property map of a field.Furthermore, the present invention relates to the use of crop property distribution data or biomass distribution data and / or soil data of a field, and the use of the soil property map. [Background technology]

[0002] The general background of the present disclosure is the treatment of plants in a field, which may be a field, a greenhouse, etc. The treatment of plants, such as cultivated crops, may also include the treatment of weeds present in the field, the treatment of insects, the treatment of pathogens present in or present in the field.

[0003] It has been found that a need exists to provide information to control treatment equipment for treating a field.

[0004] An important type of such information relates to the soil properties of the field, so that the control of the treatment equipment can take the soil properties into account, thereby resulting in a more ecological and / or economical treatment.

[0005] In order to make agriculture more sustainable and reduce the impact on the environment, precision agriculture technologies are being developed. In this regard, semi-automated or fully automated plant treatment devices such as drones, robots, smart sprayers, etc. can be configured to treat plants, weeds, insects and / or pathogens in fields based on ecological and economic rules. Technological developments in the field of drones or robots enable new treatment schemes for farmers.

[0006] The disclosed method enables sustainability of highly accurate and zone-specific agricultural management, soil management and soil treatments by combining remotely measured crop information with on-ground soil information, in particular for seeding density and / or crop nutrition recommendations. Summary of the Invention [Means for solving the problem]

[0007] In one aspect of the present disclosure, a computer-implemented method for generating a soil property map of a field is provided, the method comprising: - receiving crop characteristic distribution data for a field, the crop characteristic distribution data including at least one crop-related parameter; - determining equivalent regions having crop-related parameter values ​​within a predetermined range of the crop characteristic distribution data; - receiving soil data relating to at least one soil parameter for each of the determined equivalent areas; - generating a soil property map of the field based on the soil data and the corresponding area; Includes.

[0008] In a further aspect of the present disclosure, a system for generating a soil property map of a field is provided, the system comprising: - a first receiving unit configured to receive crop characteristic distribution data for a field, the crop characteristic distribution data including at least one crop-related parameter; - a determining unit configured to determine an equivalent region having a crop-related parameter value within a predetermined range of the crop characteristic distribution data; - a second receiving unit configured to receive soil data relating to at least one soil parameter for each of the determined equivalent areas; - a generating unit configured to generate a soil property map of the field based on the soil data and the corresponding area; Equipped with.

[0009] In another aspect of the present disclosure, a computer program element is disclosed that includes instructions configured to perform steps of a method according to the present disclosure in a system according to the present disclosure when executed on a computing device of a computing environment.

[0010] In a further aspect of the present disclosure, the use of crop specific distribution data or biomass distribution data and / or soil data of a field in a method according to the present disclosure is provided.

[0011] In a further aspect of the present disclosure, the use of a soil property map provided in accordance with a method according to the present disclosure to provide treatment instruction data for an agricultural machine to treat a field is presented.

[0012] Any disclosures and embodiments described herein relate to the methods, systems, computer program elements and uses outlined above, and vice versa. Advantageously, benefits provided by any of the embodiments and examples apply to all other embodiments and examples as well, and vice versa.

[0013] As used herein, "determining" also includes "initiating or causing a decision," "generating" also includes "initiating or causing a generation," and "providing" also includes "initiating or causing a determination, generation, selection, transmission, or reception." "Initiating or causing the performance of an action" includes any processing signal that triggers a computing device to perform the respective action.

[0014] The methods, systems, computer program elements and uses disclosed herein provide an efficient, sustainable and robust method for treating fields. In particular, soil property maps of a field can be provided at reduced cost while maintaining or even improving accuracy to enable improved soil management, which in turn improves the sustainability of soil treatment.

[0015] The basic idea of ​​the present disclosure is to determine areas of equivalence using crop characteristic distribution data, the crop characteristic distribution data including at least one crop-related parameter. This means that areas of a field having the same or similar crop-related parameter are considered to be equivalent. In this context, crop-related parameters may be considered similar if their difference is less than a predefined absolute or relative amount. Furthermore, a second crop-related parameter may be considered similar to a first crop-related parameter if it is within a predefined range around the first crop-related parameter. In particular, crop-related parameters may be considered equivalent if they are within a predefined / predefined range, such as, for example, + or -50%, more preferably + or -40%, most preferably + or -30%, particularly preferably + or -20%, in particular + or -10%, of the corresponding crop-related parameter value of the crop-related parameter. Furthermore, crop-related parameters may be considered similar if their difference is less than 50%, preferably less than 40%, more preferably less than 30%, particularly preferably less than 20%, in particular less than 10%. This leads to the fact that by using soil data relating only to equivalent areas it is possible to generate accurate soil property maps.

[0016] Thus, the amount of soil data required to generate a soil map may be significantly reduced compared to known approaches in which a field is divided into multiple zones and soil data must be received for each of these zones, and as a result the work to generate this soil data may be reduced.

[0017] In an exemplary embodiment, a known method for providing a soil property map includes dividing a field in 10 zones and taking soil samples from each of these zones. A soil property map is then generated based on soil data derived from the soil samples. For the same field, using the disclosed method, only three equivalent areas may be determined. Therefore, in this respect, only three soil samples are needed, one for each equivalent area. Thus, in this embodiment, soil samples at and around the three locations that best represent the equivalent areas are needed. In this way, a soil property map can be generated with reduced effort.

[0018] The object of the present invention is to provide an efficient, sustainable and robust field treatment approach. These and other objects will become apparent on reading the following description and are solved by the subject matter of the independent claims. The dependent claims refer to preferred embodiments of the invention.

[0019] definition The term processing device should be understood broadly in this case and includes any device configured to process a field. The processing device may be configured to traverse the field. The processing device may be a ground or air vehicle, such as a rail vehicle, a robot, an airplane, an unmanned aerial vehicle (UAV), a drone, etc. The processing device may be equipped with one or more processing units and / or one or more monitoring units. The processing device may be configured to collect field data via the processing units and / or the monitoring units. The processing device may be configured to sense field data of the field via the monitoring units. The processing device may be configured to process the field via the processing units. The processing units may operate based on monitoring signals provided by the monitoring units of the processing device. The processing device may comprise a communication unit for connection. Via the communication unit, the processing device may be configured to provide or transmit field data, to provide or receive operational data, and / or to provide or receive operational data.

[0020] A treatment action, as used herein, refers to data that characterizes the operation of a treatment device to treat a field, specifically the field conditions of the field. A treatment action identifier can indicate a treatment action. A treatment action can be characterized by a treatment type and / or a treatment mode. A treatment type can refer to an application type such as seeding, harvesting, chemical application, with seeding being a preferred treatment type. A treatment mode refers to a mode or class of treatment indicators for a single treatment type. For a chemical application treatment such as spraying, the mode can be spraying herbicide A, spraying fungicide A, spraying insecticide A, flat spray, spot spray, etc. A treatment action may include any data for characterizing, selecting, activating, or operating a treatment device to treat a field.

[0021] A monitoring operation, as used herein, refers to an operation of a processing device to monitor a field, and in particular the collection of field data for the field. A monitoring operation identifier may indicate a monitoring operation. A monitoring operation may be characterized by a monitoring type and / or a monitoring mode. A monitoring type refers to a monitoring indicator, such as plant sensing for weed treatment, soil sensing for seeding, etc. A monitoring mode may refer to a mode of a single monitoring type or a class of modes. In the case of plant sensing, the mode may be weed image detection, crop image detection, fungus optical detection, etc. A monitoring operation may include any data for characterization, activation, or operation of a processing device to monitor a field.

[0022] The term treatment as used herein may relate to any treatment for the cultivation of plants. The term treating or treatment should be understood in the broad sense in this case and relate to any treatment of a field, such as sowing, applying a product, harvesting, etc.

[0023] The term treatment product should be understood to be any object or material that is useful for treatment. In the context of the present invention, the term treatment product is not intended to be limiting. - Chemical products such as fungicides, herbicides, insecticides, acaricides, molluscicides, nematicides, birdicides, pisciicides, rodenticides, insect repellents, bactericides, biocides, safeners, plant growth regulators, urease inhibitors, nitrification inhibitors, denitrification inhibitors or any combination thereof; - biological products such as microorganisms useful as fungicides (bio-fungicides), herbicides (bio-herbicides), insecticides (bio-insecticides), acaricides (bio-acaricides), molluscicides (bio-molluscicides), nematicides (bio-nematicides), birdicides, pisciicides, rodenticides, insect repellents, fungicides, biocides, safeners, plant growth regulators, urease inhibitors, nitrification inhibitors, denitrification inhibitors, or any combination thereof; - fertilizers and compost, - seeds and seedlings, - Water, as well as - including any combination thereof, in this disclosure the treated product is preferably a fertilizer product, a compost product and / or a seed or seedling.

[0024] A distributed computing environment, as used herein, refers to a distributed machine setup that includes multiple processing devices for processing a field. The multiple processing devices may be interconnected. The multiple processing devices may be connected via a distributed computing device. The computing device may be part of the processing device and / or may be remote from the processing device connected through a network.

[0025] The term field as used herein refers to a field to be treated. A field can be any plant or crop growing area, such as a farm, a greenhouse, etc. The plants can be crops, weeds, volunteer plants, crops from a previous growing season, useful plants or any other plants present in the field. A field can be identified through its geographic location or georeferenced location data. Reference coordinates, size and / or shape can be used to further identify the field.

[0026] Field data as used herein should be understood broadly in this case and includes any data that can be acquired by the processing device. The field data can be acquired from a processing unit and / or a monitoring unit of the processing device. The field data can include measurement data acquired by the processing device. The measurement data can include data related to field conditions and / or operation of the processing device with respect to the field. The field data can include image data, spectral data, plot data indicating flagged plots, derived plant data, derived crop data, derived weed data, derived soil data, topographical data, trajectory data of the processing device, measured environmental data (e.g., humidity, airflow, temperature and solar radiation), and transactional data related to processing operations. The field data can be associated with the plots, such as location or position data of the plots.

[0027] A field condition as used herein may relate to a condition detected on a field, including treatment or monitoring on the field. A field condition may be derived from measurement data. The field data may include a field condition. A field condition may indicate the presence of a certain treatment requirement, such as the presence of a certain weed, insect, or fungus in a section of the field. A field condition may relate to a status flag of the section of the field. The status flag may be "to be treated", "not treated" or "treated". The status flag "to be treated" may relate to the detection of a field condition indicating that treatment is required. For example, weeds, fungi, nutrient levels, water levels, growth stage or any other condition may be detected on the field requiring treatment, for example by a monitoring unit of the treatment device. The status flag "treated" may relate to a treatment status of the field indicating that treatment has been performed. The status flag "not treated" may relate to the detection of a field condition indicating that treatment is not required.

[0028] A plot of a field should be understood in the broad sense in this case and relates to at least one position or location on the field. A plot may relate to a zone of the field including multiple positions or locations on the field forming a continuous area of ​​the field. A plot may relate to a distributed patch of multiple positions or locations on the field exhibiting a common field condition. A plot may be flagged indicating the field condition of the plot. A plot may include one or more positions or locations on the field flagged with one or more flags indicating a field condition. A field may include one or more plots. A plot may relate to field data, in particular field conditions. A plot may be flagged. A plot may be identified through its geographic location or georeferenced location data. Reference coordinates, size and / or shape may be used to further identify the plot.

[0029] Operational data as used herein should be understood broadly in this case and relate to any data configured to operate a processing device. The term operational data refers to data configured to operate at least one processing device in relation to other processing devices. The operational data may be configured to control one or more technical means of the processing device. The operational data may include data for controlling the processing and / or monitoring units of the processing device. The operational data may be configured to control the movement of the processing device. The operational data may be configured to control the steering and drive units of the processing device. The operational data may be configured to control one processing device in relation to other processing devices.

[0030] In one embodiment, the method further comprises the step of generating treatment instruction data for the agricultural machine based on the soil property map. Thus, treatment by the agricultural machine can be performed according to the soil data provided in the soil property map. This means that different equivalent areas can be treated in different ways. This is both economically and environmentally efficient while improving crop quality and crop yield. This is due to the fact that applying the method allows the right treatment using the right agricultural machine and the right treatment product in the right amount.

[0031] In another embodiment, the crop characteristic distribution data is biomass distribution data and the crop-related parameter values ​​are biomass values. Such crop characteristic distribution data can be generated in a simple and cost-effective manner, for example, based on images taken by satellites or unmanned aerial vehicles. This also shows that the method of the present disclosure is simple and cost-effective. At the same time, the biomass distribution data and the biomass values ​​are reliable and accurate parameters for determining the equivalent area. This leads to a reliable and accurate soil property map. Furthermore, the biomass values ​​and the corresponding biomass distribution data can be analyzed in a computationally efficient manner.

[0032] According to a further embodiment, the biomass distribution data is derived from the yield distribution data of the field. The yield distribution data is relatively easy to generate, for example by a processing device. Moreover, the yield distribution data is a reliable and sufficiently accurate indicator of the biomass distribution data. In this way, the biomass distribution data can be generated in a simple and reliable manner.

[0033] In another embodiment, the biomass distribution data is derived from satellite imagery data of the field. Such images are available from satellite operating organizations at relatively low cost and with high accuracy. Thus, procuring satellite images is simple and cost-effective. These images can be analyzed to derive the biomass distribution. To achieve this, standard image analysis techniques can be used, which are sufficiently fast, reliable and accurate. In summary, biomass distribution data can be provided in a fast, simple and reliable manner.

[0034] In a further embodiment, the biomass distribution data is derived from plant data of the field. Plants form part of the biomass, in particular plants form the vast majority of the biomass on the field. Thus, accurate and reliable biomass distribution data can be generated by aggregating the plant data, which is simple and reliable.

[0035] In alternative embodiments, the biomass distribution data is received from a database, is a current measurement, or is user input. Of course, these alternatives can also be combined. These are all reliable sources of biomass distribution data that can be accessed in a simple manner.

[0036] In one embodiment, the biomass distribution data is derived from historical biomass distribution data. The historical biomass distribution data is for at least 2 years, more preferably at least 4 years, even more preferably at least 8 years, and most preferably at least 10 years. The advantage is that no actual analysis of the field, e.g. by measuring or taking images, is required for the generation of the current biomass distribution data. The larger the base of historical biomass distribution data, the more accurate the derived biomass distribution data. This leads to efficient generation of reliable and accurate soil property maps.

[0037] In another embodiment, the method includes providing soil sampling location data of the field based on the determined equivalent area. Preferably, soil sampling path data for a soil sampling device through the field is provided and / or soil sampling map data of the field is provided. The soil sampling location data, soil sampling path data and soil sampling map data may be summarized as operation data or operation instructions for the soil sampling device. Simply, using this operation data consists in instructing the soil sampling device. Providing this data therefore provides a basis for efficiently operating the soil sampling device, i.e., for efficiently taking soil samples and subsequently providing soil data. It is reiterated that sampling is based on the equivalent area, and therefore fewer samples need to be taken compared to sampling in a regular terrain pattern. For example, the soil sampling location data may include one sampling location information for each equivalent area. Thus, the soil sampling path data may describe the shortest and fastest route to connect the sampling locations.

[0038] In a further embodiment, the sampling location data comprises 1 to 10, preferably 2 to 7, more preferably 4 to 6, most preferably only one soil sampling location for each equivalent area. This has been found to be a good compromise between the effort to take soil samples and provide corresponding soil data on the one hand and the accuracy and reliability of the soil property map on the other hand.

[0039] In another embodiment, the method further comprises providing soil sampling timing data with respect to the time at which the soil sample has to be taken. Thus, when taking the soil sample, dynamic processes in the soil, which may be running at different time series, can be taken into account. For example, samples taken to evaluate soil properties related to the use of fertilizers, such as phosphate fertilizers or any other fertilizer of interest, may be valid for 3-5 years. In this way, the soil sampling timing data may take into account the timing data of previous samples. The same applies to samples taken to evaluate properties related to pH values ​​or field liming. Such samples may also be valid for 3-5 years. In contrast, samples taken to evaluate soil properties related to, for example, nitrogen, are generally valid for a short period of time and may need to be measured more frequently, for example, 1 year or less. In this way, the sampling timing data can be adapted individually. The use of the sampling timing data helps to receive reliable and accurate soil data, thus leading to the generation of reliable and accurate soil property maps.

[0040] Any one of the preceding methods further comprising the step of providing soil sampling method data. Using the soil sampling method data, specific soil sampling methods may be suggested. Also, a list of suitable soil sampling methods may be provided, such as, for example, proximity soul sampling / sensing methods (e.g., ground penetrating radar, electromagnetic induction, electrical resistivity, magnetometer, magnetic susceptibility, X-ray fluorescence, mechanical interaction, ion selective potentiometry, seismometer, gamma ray, etc.). This ensures that accurate and relevant soil data is received. As a result, a high quality soil property map may be generated.

[0041] Any one of the preceding methods, wherein the soil data is or relates to soil organic matter, total carbon content, organic carbon content, inorganic carbon content, soil humus content, boron content, phosphate content, potassium content, nitrogen content, sulphur content, calcium content, iron content, aluminium content, chlorine content, molybdenum content, magnesium content, nickel content, copper content, zinc content, manganese content, soil pH value of the soil of the field or field zone, soil texture, soil gravelization, soil water content, soil humidity, soil temperature, soil surface temperature, soil density, soil texture, soil conductivity, water holding capacity, clay content, silt content and / or sand content of the soil, which soil parameters affect crop quality of a crop grown in the field.

[0042] In the present disclosure, crop quality may relate to at least one of protein content, sugar content, starch content, fat content, gluten content, vitamin content, provitamin content, fatty acid content, unsaturated fatty acid content, omega-n fatty acid content, omega-3 fatty acid content, dietary fiber content, mineral content, antioxidant content, plant hormone content, and other substances that have nutritional value to humans or animals eating the corresponding crop.

[0043] In a further embodiment, the method further comprises providing control data for controlling agricultural machinery based on the soil property map and / or the treatment instruction data. The generated soil property map can be used together with the seed data or nutrient data or crop protection data or weed management data to generate instruction maps for zone-based seeding or zone-based fertilizer or zone-based fungicide, insecticide, and pesticide or zone-based herbicide spraying. All of the above treatments require zone-specific characteristics of soil properties for sustainable use and application rates.

[0044] In another embodiment, the system according to the present disclosure comprises a further generating unit configured to generate processing instruction data for the agricultural machine based on the soil property map. In this way, the soil property map is directly used for the processing or processing operation. The generation of the processing instruction data and the provision of the processing instruction data to the agricultural machine are preferably performed automatically. As a result, an appropriate processing can be determined in a fast and reliable manner. This also leads to an improvement in the quality of the crop.

[0045] In a further embodiment, the system further comprises a providing unit for providing control data for controlling the agricultural machinery based on the soil property map and / or the processing instruction data. The zone specific instruction map generated by the soil property map paired with the transaction data can be transferred, for example via pen drive, Wi-Fi or other data transfer methods, to an agricultural monitoring device of a tractor steering an agricultural machinery implement such as a seed drill for seeding treatment or a fertilizer spreader for nutrient treatment or a sprayer for crop protection and weed management.

[0046] The methods disclosed herein may further include transferring the field data and / or operational data to a remote computing device for storing the field data and / or operational data for further data processing. Due to the limited storage capacity of processing devices and enhanced utilization of big data, remote storage capacity is beneficial. To reduce the impact of such transfer on processing capacity, the transfer may be performed by batch data processing.

[0047] In the following, the present disclosure will be described in detail with reference to the accompanying drawings. [Brief description of the drawings]

[0048] [Figure 1] 1 illustrates an exemplary embodiment of a system with multiple UAVs for the treatment of a field. [Diagram 2] FIG. 1 is a schematic diagram of a UAV adapted for treating a field. [Diagram 3] FIG. 1 is a schematic diagram of a ground robot adapted for treating a field. [Figure 4] 1 illustrates an embodiment of a system for generating a soil property map of a field. [Diagram 5] 5 shows a field equivalent area and soil property map generated by the system of FIG. 4. [Figure 6] 1 shows a flow diagram of an example method for generating a soil property map of a field. [Figure 7] 5A-5C show soil property maps produced by the system of FIG. 4 according to different embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0049] The present disclosure is based on the finding that a field comprises heterogeneous features (e.g. soil, plants, weeds, etc.) that are distributed throughout the field. These features are not permanent and therefore are not fully known before the treatment device treats the field. For example, monitoring with the means of the treatment device during the agricultural treatment process reveals these specific features of the field at least in part. This advantageous information on these specific features usefully helps to improve the treatment strategy of one or more further treatment devices. In so doing, it is possible to react on demand to changing conditions in the field. In other words, the method collects field data, for example, via the means of the treatment device passing through the field, and provides operational data to the same or further treatment devices based on the field data. This allows for demand-driven treatment of the field. This advantageously increases the efficiency of the treatment.

[0050] The following embodiments are merely examples for implementing the methods, systems, computer elements, or uses disclosed herein and should not be considered as limiting.

[0051] FIG. 1 illustrates an exemplary embodiment of a system having multiple UAVs as processing devices.

[0052] The system of FIG. 1 illustrates a distributed system including multiple UAVs 102, 104, 106, one or more ground stations 110, one or more user devices 108, and a cloud environment 100. The UAVs 102, 104, 106 are unmanned aerial vehicles that may be controlled autonomously by an on-board computer, remotely by a pilot controller, or partially remotely, for example, via initial operation data. The UAVs 102, 104, 106 may transmit data signals collected from various on-board sensors and actors mounted on the UAVs. Such data may include current flight data, such as current altitude, speed, battery level, position, weather, or wind speed; field data including processing operation data, such as processing type, processing location, or processing mode; monitoring operation data, such as field condition data or position data; and / or operation data, such as initial operation data, updated operation data, or current operation data. The UAVs 102, 104, 106 can directly or indirectly transmit data signals, such as field data or operational data, to the cloud environment 100, the ground station 110, or other UAVs 102, 104, 106. The UAVs 102, 104, 106 can directly or indirectly receive data signals, such as field data or operational data, from the cloud environment 100, the ground station 110, or other UAVs 102, 104, 106.

[0053] More generally, each of the UAVs 102, 104, 106 may be referred to as agricultural machinery.

[0054] The cloud environment 100 can facilitate data exchange with and between the UAVs 102, 104, 106, the ground control station 110, and the user device 108. The cloud environment 100 can be a server-based distributed computing environment for storing and computing data on multiple cloud servers accessible from the Internet. The cloud environment 100 can be a distributed ledger network that facilitates a distributed immutable database for transactions performed by the UAVs 102, 104, 106, one or more ground stations 110, or one or more user devices 108. A ledger network refers to any data communication network that includes at least two network nodes. The network nodes can be configured to a) request data inclusion with a data block, and / or b) verify requested data inclusion in a chain, and / or c) receive chain data. In such a distributed architecture, the UAVs 102, 104, 106, one or more ground stations 110, and one or more user devices 108 can function as nodes that store transaction data in a data block and participate in a consensus protocol to verify transactions. When at least two network nodes are in a chain, the ledger network may be referred to as a blockchain network. The ledger network 100 may consist of a blockchain or cryptographically linked list of data blocks created by the nodes. Each data block may contain one or more operations on field data or operational data. Blockchain refers to a continuously extensible data set provided in multiple interconnected data blocks, each data block may contain multiple transaction data. The transaction data may be signed by the transaction owner, and the interconnection may be provided by chaining using cryptographic means. Chaining is any mechanism for interconnecting two data blocks with each other. For example, in a blockchain, at least two blocks may be directly interconnected with each other.A hash function cryptographic mechanism may be used to chain data blocks in a blockchain and / or to append new data blocks to an existing blockchain. A block may be identified by its cryptographic hash, which references the hash of a preceding block.

[0055] The UAVs 102, 104, 106 and the ground station 110 can share data signals with the user device 108 via the cloud environment 100. Communication channels between the nodes and between the nodes and the cloud environment 100 can be established through wireless communication protocols. Cellular networks can be established for communication from the UAVs 102, 104, 106 to the UAVs 102, 104, 106, from the UAVs 102, 104, 106 to the ground station 110, from the UAVs 102, 104, 106 to the cloud environment 100, or from the ground station 110 to the cloud environment 100. Such cellular networks may be based on any known network technology, such as SM, GPRS, EDGE, UMTS / HSPA, LTE technology, using standards such as 2G, 3G, 4G, or 5G. In a local area of ​​the field 112, a wireless local area network (WLAN), such as, for example, Wireless Fidelity (Wi-Fi), may be established for communication from the UAVs 102, 104, 106 to the UAVs 102, 104, 106 or from the UAVs 102, 104, 106 to the ground station 110. The cellular network from the UAVs 102, 104, 106 to the UAVs 102, 104, 106 or from the UAVs 102, 104, 106 to the ground station 110 may be a flying ad hoc network (FANET).

[0056] The first UAV 102 may be configured to perform a first processing operation, and the second UAV 104 may be configured to perform a second processing operation. Preferably, the processing operations of the UAVs 102, 104, 106 can differ in terms of processing type or processing mode. The term processing type relates to the application principle used. The application type can include sowing, harvesting, pesticide application, etc. The processing mode for pesticide application can be a spray mode (e.g., flat, spot, variable rate), the processing mode for mechanical application can be a removal mode (e.g., gatherer, cutter), and the processing mode for electrical application can be a current application mode (e.g., laser, voltage pulse). The term processing type relates to a weed class and a corresponding herbicide class, fungicide class, pesticide class. Furthermore, the term processing type relates to a plant and a corresponding fertilizer class.

[0057] In an exemplary embodiment, the first UAV 102 carries a different treatment product than the second UAV 106. For example, the first UAV 102 is a scout and sprayer UAV configured to sense field conditions and apply the first treatment product. For example, the second UAV 104 is a spray drone that applies the second treatment product. The first UAV 102 collects field data along its trajectory. For example, the distribution of weeds, fungi, or insects or the type of weeds, fungi, or insects to be treated with the second treatment product is collected in association with position data along the trajectory of the scout and spray drone. Based on the field data, operational data for the second UAV 104 is determined. For example, based on the distribution of the detected weeds, fungi, or insects and the type of the detected weeds, fungi, or insects, the location to be treated with the second treatment product is identified and operational data for the second UAV 104 carrying the second treatment product is determined. The operational data is provided to the second UAV 104. The operational data may be initial or adapted operational data of the second UAV. For example, the trajectory may be adapted depending on the location of the weeds, fungi or insects to be treated. That is, if the first UAV 102 determines that it does not have the necessary treatment product or does not carry the technical equipment required for treatment, the second UAV 104 can be called to the area 113, and the second UAV 104 carries the necessary product or technical equipment. For example, if a first UAV 102 detects weeds, mold or insects in a section 113 of a field 112 and determines that it is not carrying the required herbicide, fungicide or pesticide, the above method provides information and / or derived instructions to a second UAV 104 or further UAV 106 that is carrying the required herbicide, fungicide or pesticide so that the second and / or further UAV 104, 106 can eradicate the weeds, mold or insects in the section 113.

[0058] In another embodiment, the first UAV 102 may just not have enough processing product and cannot process the entire section 113 of the field 112. In this embodiment, the method provides information of the section 113 not yet processed by the first UAV 102 to the second and / or further UAVs 104, 106 to process the section 113. In another embodiment, the first UAV 102 may only monitor the section 113 of the field 112, for example to provide field data, and the method then provides operational data based on the field data to the second and / or further processing devices 102, 106. In this embodiment, the first UAV 102 functions as a reconnaissance drone.

[0059] In these examples, the operational data determination may be implemented locally on the computing device of the first UAV 102, the second UAV 104, or the ground station 110. The operational data determination may be implemented using the distributed cloud environment 100. The field data and / or operational data may be provided through real-time and / or batch data processing. To reduce delays and conserve bandwidth, smart data transfer management is beneficial. The field data may be batched for processed parcels 113 and processed in real-time for unprocessed parcels 113. The operational data may be batched for processed parcels and processed in real-time for unprocessed parcels 113. The operational data for unprocessed parcels 113 may be provided to the processing device in real-time. The field data for unprocessed parcels 113 may be provided to the operational data determination unit in real-time. Real-time as used herein refers to data transfer that is not actively stalled or queued. Data transfer is considered real-time when the delay in the transfer is due to the processing or transfer capabilities of the processing system involved.

[0060] The operational data in these embodiments specifies whether the UAVs 104 operate in a sequential mode or a simultaneous mode. In the sequential mode, the first UAV 104 traverses the field 112 or a section 113 of the field 112 before the second UAV 104 or the other UAV 106 begins processing the field 112. In the simultaneous mode, the first, second and other UAVs, UAVs 102, 104, 106, operate simultaneously on the field 112. The simultaneous mode can be achieved via a swarm algorithm, a fuzzy logic algorithm or any other algorithm that operates a self-organizing population system.

[0061] In this regard, it should be noted that the description applies to any number of processing devices having different hardware characteristics. For example, instead of one first UAV 102, a first group of UAVs 102 can detect field conditions and process the field 112 according to the operational data, and the first UAV 102 is equivalent. The same may apply to a second UAV 104, where the second group of UAVs 104 can detect field conditions and process the field 112 according to their operational data, and the first UAV 102 is equivalent. In addition, the number of UAVs 102, 104, 106 operating on the field 112 can be adjusted according to the demand derived from the field data. Thus, the operational data can include instruction data or logic for orchestrating the operation of two or more UAVs 102, 104, 106 on the field 112. For example, a first group of UAVs 102 may be operated in swarm mode, a second group of UAVs may be operated in swarm mode, and the second group of UAVs 104 may be operated in sequential mode relative to the first group of UAVs 102.

[0062] The treatment devices on the field 112 may include additional ground vehicles or other aircraft (not shown). The treatment devices may include a robot (FIG. 3), a tractor, a harvester, a seed sower, a sprayer, or any other agricultural vehicle. The treatment devices may be configured to monitor the field. The treatment devices may be configured to apply any treatment product (e.g., water, herbicide, fungicide, fertilizer, seeds). The treatment devices may be configured to prepare the field 112 for seeding. The treatment devices may be configured to harvest plants in the field 112. In the present disclosure, the treatment devices are preferably seed sowers and the treatment products are preferably seeds.

[0063] FIG. 2 shows UAVs 102, 104, 106 in flight adapted to treat a field 112.

[0064] The UAVs 102, 104, 106 shown in this example include a camera as a monitoring unit 124 for monitoring field conditions and two spray nozzles as treatment units 120, 122 for spraying treatment products. The spray nozzles 120, 122 are in fluid communication with at least one tank carried by the UAVs 102, 104, 106. Such a setup allows for more efficient and targeted field treatment, since the treatment units 120, 122 can be triggered to treat the field 112 depending on the monitored field conditions. Both operations may be performed while the UAVs 102, 104, 106 hover above their respective field plots 113. In other embodiments, the UAVs 102, 104, 106 may be reconnaissance UAVs 102, 104, 106 that include a monitoring unit 124 for monitoring field conditions. In other embodiments, the UAV 102, 104, 106 may be a spray UAV 102, 104, 106 that includes a treatment unit 120, 122 for spraying a treatment product.

[0065] The processing units 120, 122 may alternatively be seeding units, releasing units, collecting units, harvesting units, and the like.

[0066] The operational data may be configured to control the processing units 120, 122. The monitoring units 124 may include optical sensors (e.g., cameras, NIR sensors, RGB cameras, LIDAR, LADAR, RGB, lasers), GPS sensors, temperature sensors, humidity sensors, solar radiation sensors, air current sensors, coverage rate sensors, wind speed sensors, wind direction sensors, etc. The operational data may include data for controlling the monitoring units 124.

[0067] FIG. 3 shows ground robots 102 , 104 , 106 adapted to treat a field 112 .

[0068] In contrast to the UAVs 102, 104, 106 of Figures 1 and 2, the treatment equipment, i.e., the ground robots 102, 104, 106 of Figure 3, are ground-based and traverse the ground. As shown in this example, the robots 102, 104, 106 include a monitoring unit 124 for monitoring the field conditions and spray nozzles 122, 124 as treatment units for spraying the treatment product. The spray nozzles 120, 122 are in fluid communication with at least one tank carried by the robots 102, 104, 106. Similar to the UAVs 102, 104, 106 in flight, such a ground-based setup allows for more efficient and targeted field treatment, since the nozzles 122, 124 can be triggered to treat the field 112 depending on the monitored field conditions. In other embodiments, the robots can be reconnaissance robots, including the monitoring unit 124 for monitoring the field conditions. In another embodiment, the robot may be a spraying robot including treatment units 122, 120 for spraying a treatment product.

[0069] Needless to say, the ground robots 102, 104, and 106 can also be referred to as agricultural machinery.

[0070] 4 illustrates a system 126 for generating a soil property map SPM of a field 112. An exemplary soil property map SPM is shown in FIG.

[0071] The system 126 includes a first receiving unit 128 configured to receive crop characteristic distribution data D of the field 112. The crop characteristic distribution data D includes at least one crop-related parameter P.

[0072] In the example shown in Figures 4 and 5, the crop characteristic distribution data D are biomass distribution data BMD and the crop related parameter P values ​​are biomass values ​​BMV.

[0073] The biomass distribution data BMD is received from the database 130 or is current measurements performed by, for example, the agricultural machines 102, 104, 106 described above, or is user input IP.

[0074] The database 130 may be part of the cloud environment 100. Alternatively, the database 130 may form part of the ground station 110 (see FIG. 1). In this example:

[0075] In another embodiment, the biomass distribution data BMD is derived from historical biomass distribution data occurring over the past 10 years. The historical biomass distribution data is also provided by the database 130.

[0076] The system 126 further comprises a determining unit 132 configured to determine equivalence areas A1, A2, A3, A4 having values ​​of the crop-related parameter P within a predetermined range of the crop characteristic distribution data D.

[0077] In this embodiment, the determining unit 132 is configured to determine equivalent areas A1, A2, A3, A4 having biomass values ​​BMV within a predetermined range of the biomass distribution data BMD.

[0078] This is illustrated in FIG. 5, which shows a total of four equivalent areas A1, A2, A3, A4 of the field 112.

[0079] Equivalence region A1 has biomass values ​​BMV within a first range, equivalence region A2 has biomass values ​​BMV within a second range, equivalence region A3 has biomass values ​​BMV within a third range, and equivalence region A4 has biomass values ​​BMV within a fourth range.

[0080] Generally, it is derived from the biomass distribution data BMD and the biomass values ​​BMV are considered to be uniform within each equivalence area A1, A2, A3 and A4.

[0081] It is noted that the portions in one of the equal regions A1, A2, A3 and A4 do not necessarily have to be contiguous.

[0082] The system 126 additionally comprises a second receiving unit 134 arranged to receive the soil data SD.

[0083] The soil data SD comprises at least one soil parameter SP for each of the determined equivalent areas A1, A2, A3, A4, the soil parameter SP being, for example, the phosphate content.

[0084] Attention is paid to the fact that in this example the soil parameter SP related to the phosphorus content is of merely illustrative nature, the soil parameter could also relate to any of the other alternatives already explained above.

[0085] The soil parameter SP is determined, for example, by taking soil samples at each of the sampling locations L1, L2, L3, L4, L5, L6, and L7 shown in FIG.

[0086] In this example, for the equivalent area A1, two samples are taken, namely at locations L1 and L4.

[0087] In the equivalent area A2, two samples are also taken, namely at locations L2 and L7.

[0088] In the equivalent area A3, two samples are also taken, namely at locations L3 and L6, respectively.

[0089] In the equivalent area A4, only one sample is taken at location L5.

[0090] The system 126 further comprises a generation unit 136 configured to generate a soil property map SPM of the field 112 based on the soil data SD and the equivalent areas A1-A4.

[0091] This means that in the example of Figure 5, the phosphate content (or any other parameter mentioned above) derived from samples taken at locations L1 and L4 is attributed to the entire equivalent area A1. If different phosphate contents are detected at locations L1 and L4, the average phosphate content can be used.

[0092] Similarly, the phosphate content (or any other above mentioned parameter) derived from samples taken at locations L2 and L7 is imputed to the entire equivalent area A2.

[0093] Similarly, the phosphate content (or any other above mentioned parameter) derived from samples taken at locations L3 and L6 is imputed to the entire equivalent area A3.

[0094] The phosphate content (or any other above mentioned parameter) derived from the sample taken at location L5 is imputed to the entire equivalent area A4.

[0095] The system 126 additionally comprises a further generation unit 138 configured to generate processing instruction data TD for the agricultural machine based on the soil property map SPM.

[0096] The agricultural machine is, for example, one of agricultural machines 102, 104, 106.

[0097] In this embodiment, the exemplary soil parameter SP is illustratively the phosphorus content, so that the processing instruction data TD may illustratively include instructions for providing phosphate fertilizer to equivalent areas A1-A4 having a low phosphorus content.

[0098] The system 126 can be used to execute a computer-implemented method for generating a soil property map SPM of the field 112. The steps of the method are illustrated in FIG.

[0099] In a first step S1 crop characteristic distribution data D for a field 112, comprising at least one crop related parameter P, is received.

[0100] As mentioned above, in this embodiment, the crop characteristic distribution data D is biomass distribution data BMD and the crop related parameter values ​​are biomass values ​​BMV.

[0101] The biomass distribution data BMD is received from the database 130 or is, for example, a current measurement performed by one of the agricultural machines 102, 104, 106, or is a user input IP.

[0102] In an alternative embodiment, the biomass distribution data BMD is derived from historical biomass distribution data, as previously described.

[0103] In a second step S2, equivalence areas A1, A2, A3, A4 having values ​​of the crop-related parameter P within a predetermined range of the crop characteristic distribution data D are determined.

[0104] This means that in this embodiment equivalence areas A1, A2, A3, A4 are determined (see FIG. 5), each of these equivalence areas A1, A2, A3, A4 having values ​​of the crop-related parameter P within a predetermined range of the crop characteristic distribution data D.

[0105] In this example, equivalent areas A1, A2, A3, A4 are determined to have biomass values ​​BMV within a predetermined range of the biomass distribution data BMD.

[0106] More specifically, equivalence area A1 has biomass values ​​BMV within a first range, equivalence area A2 has biomass values ​​BMV within a second range, equivalence area A3 has biomass values ​​BMV within a third range, and equivalence area A4 has biomass values ​​BMV within a fourth range.

[0107] Generally, it is derived from the biomass distribution data BMD and the biomass values ​​BMV are considered to be uniform within each equivalence area A1, A2, A3 and A4.

[0108] After the equivalent areas A1, A2, A3, A4 are determined, in an optional step S2a, soil sampling location data SSD within the field is provided, which is provided based on the determined equivalent areas, as well as soil sampling path data for the soil sampling device, and further soil sampling map data of the field.

[0109] As previously mentioned, the soil sampling location data SSD includes location data characterizing the locations L1-L7 used to take samples.

[0110] The soil sampling path data describes a path R that connects the locations L1 through L7 in the shortest possible manner, which is shown in Figure 5 using a wavy line.

[0111] The locations L1-L7 and the route R together with the contour of the field 112 form soil sampling map data. Briefly, a map of the field 112 is provided on which the sampling locations L1-L7 are indicated and a route R is suggested along which the locations L1-L7 may be reached.

[0112] As can be seen from FIG. 5, equivalent areas A1, A2 and A3 contain two soil sampling locations, and equivalent area A4 contains one sampling location.

[0113] This is due to the fact that the ground surface of equivalent area A4 is much smaller than the cumulative ground surface of equivalent areas A1, A2 and A3.

[0114] Moreover, in optional step S2a, the method may further include providing soil sampling timing data including a reference to the time at which the soil sample should be taken, and providing soil sampling method data.

[0115] Thus, the method is configured to completely describe the sampling procedure in terms of where, when and how. This complete description allows the sampling to be performed with high accuracy. Preferably, the sampling is performed automatically.

[0116] In a third step S3, soil data SD for at least one soil parameter SP, ie in this embodiment the phosphate content, are received for each of the determined equivalence areas A1-A4.

[0117] The soil data SD is generated in conjunction with the system 126, for example by a sampling procedure as described above.

[0118] As a result of step S3, in this embodiment, the phosphoric acid contents of all of the equivalent regions A1 to A4 are determined.

[0119] A soil property map SPM of the field 112 is generated in a fourth step S4. To do this, the soil data SD, i.e. in this example the phosphate content, and the equivalence areas A1 to A4 are used.

[0120] More precisely, the determined phosphate contents are attributed to the relevant equivalent areas A1-A4 and are represented in a map.

[0121] In other words, the crop property distribution data D or the biomass distribution data BMD and / or the soil data SD of the field 112 are used in a method for generating a soil property map SPM of the field 112 .

[0122] In a fifth step S5, the method further comprises generating processing instruction data TD for the agricultural machines, such as for example the agricultural machines 102, 104, 106. The soil property map SPM is used as the basis for this step.

[0123] As mentioned above, in an exemplary embodiment, the treatment may be the application of phosphate fertilizer, and the treatment instruction data TD may include instructions for providing phosphate fertilizer to the equivalent areas A1-A4 having a low phosphate content. The treatment instruction data TD may include corresponding location data and corresponding data regarding the amount of phosphate fertilizer to be applied.

[0124] In this manner, the soil property map SPM provided by the method for generating the soil property map SPM is used to provide processing instruction data TD for agricultural machines, such as agricultural machines 102, 104, 106, for processing the field 112.

[0125] Furthermore, the method for generating a soil property map SMP may be realized by a computer program element 140 comprising instructions, which, when executed on a computing device of a computing environment, are configured to execute the steps of the above-mentioned method in the above-mentioned system 126. To achieve this, the computer program element 140 may comprise computer program units, e.g. software units, corresponding to the units of the system 126. This is illustrated in FIG.

[0126] FIG. 7 shows another embodiment of a soil property map SPM generated by the method and system 126 for generating a soil property map.

[0127] The soil property map SMP of Figure 7 differs in several respects from the SPM of Figure 5. For the remaining respects, see above.

[0128] Firstly, it concerns different fields with different topography.

[0129] Furthermore, the soil property map SPM of Figure 7 includes multiple layers LA1 to LA8. In the example of Figure 7, a total of eight layers are used, however this is merely for illustration purposes.

[0130] Each of the layers LA1 to LA8 is associated with a different soil parameter SP. Any type of soil parameter can be chosen from the above list.

[0131] This means that to generate the soil property map SPM of Figure 7, the equivalent areas are determined as described above, but then soil data SD relating to a total of eight soil parameters SP are received and for each soil parameter SP one layer of the soil property map SPM is generated.

[0132] It is noted that the method for generating the soil property map SPM can be executed on any one of the cloud environment 100, the ground station 110 and the user device 108. To achieve this, a corresponding computer program element 140 is provided on any one of the cloud environment 100, the ground station 110 and the user device 108. This means that the system 126 for generating the soil property map SPM can also form at least a part of any one of the cloud environment 100, the ground station 110 and the user device 108.

[0133] An aspect of the present disclosure relates to a computer program element configured to execute the steps of the above-mentioned method. The computer program element may therefore be stored on a computing unit of a computing device, which may also be part of an embodiment. This computing unit may be configured to execute or induce the execution of the steps of the above-mentioned method. Furthermore, the computing unit may be configured to operate the components of the above-mentioned system. The computing unit may be configured to operate automatically and / or to execute the instructions of a user. The computing unit may include a data processor. The computer program may be loaded into the working memory of the data processor. The data processor may thus be equipped to execute the method according to one of the above-mentioned embodiments. This exemplary embodiment of the present disclosure encompasses both a computer program that uses the present disclosure from the beginning and a computer program that transforms an existing program into a program that uses the present disclosure by means of an update. Furthermore, the computer program element may be capable of providing all the steps necessary to carry out the procedures of the exemplary embodiments of the above-mentioned method. According to a further exemplary embodiment of the present disclosure, a computer readable medium such as a CD-ROM, a USB stick, a downloadable executable file, etc. is presented, the computer readable medium having computer program elements stored thereon, the computer program elements being as described in the previous section. The computer program may be stored and / or distributed on a suitable medium, such as an optical storage medium or a solid-state medium, supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems, however, the computer program may also be present on a network, such as the World Wide Web, and may be downloaded from such a network into the working memory of a data processor.According to a further exemplary embodiment of the present disclosure, a medium is provided making available a computer program element for downloading, the computer program element being arranged to perform a method according to one of the aforementioned embodiments of the present disclosure.

[0134] The present disclosure has been described in conjunction with preferred embodiments as examples. However, those skilled in the art and those practicing the claimed invention will understand and implement other variations upon studying the drawings, the disclosure, and the claims. In particular, any steps specifically presented may be performed in any order, i.e., the invention is not limited to a particular order of these steps. Furthermore, it is not required that different steps be performed at a particular location or at one node of a distributed system, i.e., each of the steps may be performed at different nodes using different equipment / data processing units.

[0135] In the claims and the description, the term "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage in implementations.

Claims

1. 1. A computer-implemented method for generating a soil property map (SPM) of a field (112), comprising: receiving (S1) crop characteristic distribution data (D) for said field (112), said crop characteristic distribution data (D) comprising at least one crop-related parameter (P); determining (S2) equivalent regions (A1, A2, A3, A4) having values ​​of the crop-related parameter (P) within a predetermined range of the crop characteristic distribution data (D); receiving (S3) soil data (SD) relating to at least one soil parameter (SP) for each of said determined equivalent areas (A1, A2, A3, A4); generating (S4) a soil property map (SPM) of the field (112) based on the soil data (SD) and the equivalent areas (A1, A2, A3, A4); A method comprising:

2. The method of claim 1, further comprising the step of generating (S5) processing instruction data (TD) for an agricultural machine (102, 104, 106) based on the soil property map (SPM).

3. 2. The method of claim 1, wherein said crop distribution data (D) is biomass distribution data (BMD) and said crop-related parameter (P) values ​​are biomass values ​​(BMV).

4. 4. The method of claim 3, wherein the biomass distribution data (BMD) is received from a database (130), is a current measurement, or is a user input (IP).

5. 4. The method of claim 3, wherein the biomass distribution data (BMD) is derived from historical biomass distribution data, and the historical biomass distribution data is at least two years old, more preferably at least four years old, even more preferably at least eight years old, and most preferably at least ten years old.

6. 2. The method of claim 1, further comprising a step (S2a) of providing soil sampling location data (SSD) for the field (112) based on the determined equivalent areas (A1, A2, A3, A4), preferably comprising providing soil sampling path data (R) for a soil sampling device passing through the field (112) and / or soil sampling map data for the field (112).

7. 7. The method of claim 6, wherein said sampling location data (SSD) comprises 1 to 10, preferably 2 to 7, more preferably 4 to 6, most preferably only one sampling location (L1, L2, L3, L4, L5, L6, L7) for each equivalent area (A1, A2, A3, A4).

8. The method of claim 1 further comprising providing soil sampling timing data including a reference to the time at which the soil sample should be taken.

9. The method of claim 1 further comprising providing soil sampling method data.

10. 2. The method of claim 1, wherein the soil data is or relates to soil organic matter, total carbon content, organic carbon content, inorganic carbon content, soil humus content, boron content, phosphate content, potassium content, nitrogen content, sulfur content, calcium content, iron content, aluminum content, chlorine content, molybdenum content, magnesium content, nickel content, copper content, zinc content, manganese content, soil pH value of the soil of a field or field zone, soil quality, soil gravelization, soil water content, soil humidity, soil temperature, soil surface temperature, soil density, soil texture, soil conductivity, water holding capacity, clay content, silt content, and / or sand content of the soil.

11. A system (126) for generating a soil property map (SPM) of a field (112), comprising: a first receiving unit (128) configured to receive crop characteristic distribution data (D) of said field (112), said crop characteristic distribution data (D) comprising at least one crop-related parameter (P); a determining unit (132) configured to determine equivalent areas (A1, A2, A3, A4) having crop-related parameter (P) values ​​within a predetermined range of the crop characteristic distribution data (D); a second receiving unit (134) configured to receive soil data (SD) relating to at least one soil parameter (SP) for each of said determined equivalent areas (A1, A2, A3, A4); a generating unit (136) configured to generate a soil property map (SPM) of the field (112) based on the soil data (SD) and the equivalent areas (A1, A2, A3, A4); A system comprising:

12. 12. The system (126) of claim 11, further comprising a further generating unit (138) configured to generate processing instruction data (TD) for an agricultural machine (102, 104, 106) based on the soil property map (SPM).

13. A computer program element (140) comprising instructions configured to perform the steps of the method of claim 1 in a system according to claim 11 when executed on a computing device of a computing environment.

14. 2. Use of crop characteristic distribution data (D) or biomass distribution data (BMD) and / or soil data (SD) of the field (112) in the method of claim 1.

15. 10. Use of a soil property map (SPM) provided by claim 1 for providing treatment instruction data (TD) for agricultural machines (102, 104, 106) for treating said field (112).

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