DEVICES, SYSTEMS AND METHODS FOR DETECTING AND TREATING MACHINERY CONDITIONS

Abstract

This disclosure includes devices, methods, and systems for detecting crop loss in a combine harvester. The method may include receiving forward speed data from one or more speed detection systems. The method may include receiving crop condition data from one or more feed sensors. The method may include determining one or more crop condition states, at least partially, based on one or more of the forward speed and crop condition data. The method may include determining crop loss, at least partially, based on one or more of the crop condition states. The method may include adjusting one or more combine harvester operating parameters, at least partially, based on the determination of crop loss.

Description

Territory of Revelation

[0001] The present description concerns agricultural machinery and in particular systems and methods for detecting and treating conditions relating to agricultural machinery. Background of the Revelation

[0002] There is a wide variety of different types of agricultural machinery. Agricultural machinery includes machines such as combine harvesters, sugarcane harvesters, cotton harvesters, self-propelled forage harvesters, harvesters for various agricultural products, and windrowers. During various tasks, agricultural machinery can be exposed to adverse conditions that affect its performance and the quality of its work. Brief description

[0003] The present disclosure may include one or more of the following aspects and / or combinations thereof. Some examples include a method for detecting crop loss in a combine harvester. The method includes receiving forward speed data from one or more speed detection systems. The method includes receiving crop condition data from one or more feed data sensors. The method includes determining one or more crop condition states at least partially based on one or more of the forward speed data and the crop condition data. The method includes determining crop loss at least partially based on one or more of the crop condition states. The method includes adjusting one or more combine harvester operating parameters at least partially based on the determination of crop loss.In some examples, the crop condition data includes crop image data. In some examples, the crop condition data includes data indicating crop lying down. In some examples, a crop condition includes image data indicating crop not fed in. In some examples, the crop condition includes a condition of crop in front of the harvesting machine during a harvesting operation. In some examples, the crop condition includes a condition of crop in one or more lateral directions of a harvesting machine during a harvesting operation. In some examples, one or more combine harvester operating parameters include a header cutting height. In some examples, one or more combine harvester operating parameters include a header downward force. In some examples, one or more combine harvester operating parameters include an indication of mechanical changes.In some examples, one or more combine harvester operating parameters include header belt speed. In some examples, one or more combine harvester operating parameters include belt speed adjustment. In some examples, the procedure involves identifying one or more crop rows, where adjusting one or more combine harvester operating parameters includes adjusting the orientation of a header to align it, at least partially, with one or more crop rows. In some examples, adjusting one or more combine harvester operating parameters includes reducing the working machine's travel speed. In some examples, adjusting one or more combine harvester operating parameters includes stopping the working machine's travel.

[0004] Some examples include a system for detecting crop loss in a harvesting machine. The system comprises one or more controllers configured to receive forward speed data from one or more speed detection systems, receive crop condition data from one or more feed data sensors, determine one or more crop condition states at least partially based on one or more of the forward speed and crop condition data, determine crop loss at least partially based on the one or more crop condition states, and adjust one or more combine harvester operating parameters at least partially based on the determination of crop loss.

[0005] In some examples, one or more combine harvester operating parameters include header belt speed. In some examples, adjusting one or more operating parameters includes belt speed adjustment. In some examples, one or more controllers are configured to identify one or more crop rows, and adjusting one or more combine harvester operating parameters involves adjusting the orientation of a header to align it, at least partially, with one or more of the crop rows. In some examples, adjusting one or more combine harvester operating parameters includes reducing the working machine's travel speed. In some examples, adjusting one or more combine harvester operating parameters includes stopping the working machine's travel. Brief description of the drawings

[0006] With reference to the following description of the implementations of the revelation, together with the accompanying drawings, the aforementioned aspects of the present revelation and the manner of its attainment, as well as the revelation itself, will be better understood, whereby: Fig. 1 is a side view of an exemplary working machine configured for harvesting and processing crops at a place of operation; Fig. Figure 2 is a schematic view of an exemplary control system for the working machine; Fig. Figure 3 is a schematic view of an example of an offboard control system for the working machine; Fig. Figure 4 is a schematic view of the working machine during agricultural work; Fig. Figure 5 is a flowchart showing an exemplary procedure for detecting a blocking fault condition in a working machine; Fig. Figure 6 is a flowchart showing an example of a procedure for detecting crop loss in a machinery operation; Fig. Figure 7 is a flowchart showing an exemplary procedure for detecting draper tape breaks in a machine tool; Fig. Figure 8 is a flowchart showing an exemplary procedure for detecting a harvester header misalignment in a farm machine; Fig. Figure 9 is a flowchart showing an exemplary procedure for detecting a harvester header deployment in a machinery operation. Detailed description

[0007] The examples of the present revelation described below are not intended to be exhaustive, nor are they meant to limit the revelation to the exact forms revealed in the detailed description that follows. Rather, the examples are selected and described in such a way that other experts may recognize and understand the basic features and practices of the present revelation.

[0008] In Fig. Figure 1 shows an example of a working machine 10 (e.g., an agricultural machine such as a combine harvester). The working machine 10 comprises a chassis 12, one or more front ground engagement mechanisms 13, and one or more rear ground engagement mechanisms 14. The front and rear ground engagement mechanisms 13, 14 can be wheels or tracks that are in contact with a ground surface below and support the chassis 12 above the ground. In the example shown, the front ground engagement mechanisms 13 are coupled to a front axle 11 that extends laterally, and the rear ground engagement mechanisms 14 are coupled to a rear axle 15 that extends laterally. The front and rear axles 11, 15 each have a centerline, which in the example shown are defined as their axial centers, with the axial direction indicated by the double arrow 114. Fig. Figure 1 shows that in this illustrative example, the axial and lateral directions are perpendicular to each other. As shown in Figure 1, the axial and lateral directions are perpendicular to each other. Fig. As shown in Figure 1, the double arrow 116 represents the vertical direction, which in the example shown runs perpendicular to the axial and lateral directions.

[0009] In the example shown, the ground engagement mechanisms 13, 14 are coupled to the chassis 12 and configured to rotate to guide the working machine 10 in a forward working direction (in Fig. 1 to the left) and in other directions. In some examples, the operation of the working machine 10 is controlled from an operator's cabin 16. The operator's cabin 16 can include any number of controls for controlling the operation of the working machine 10, such as a user interface 220. In some examples, various aspects relating to the performance of the working machine 10 can be recorded to determine various conditions of the working machine 10, such as undesired header conditions, uncollected crop, draper belt breaks, misalignment during harvesting, and / or undesired folding and / or unfolding conditions of the header.

[0010] In some examples, the controller 202, 302 can determine various machine characteristics and environmental attributes that may contribute to undesirable machine conditions. The controller 202, 302 can determine the machine characteristics and / or environmental attributes, at least partially, based on sensor data relating to the machine 10. In some examples, the controller 202, 302 can determine various factors such as environmental conditions, the position of the machine 10, the terrain in which the machine is operating (e.g., slope, forward tilt, lateral tilt, etc.), soil firmness, and / or environmental attributes (e.g., dew point and / or humidity). In some examples, the controller 202, 302 can determine the presence of essentially fixed field features such as a field boundary, a field road, a ditch, a watercourse, and / or a terrace.In some examples, the control 202, 302 can essentially determine movable, non-crop objects such as rocks, flagstones, power poles, tree trunks, other machinery, and / or irrigation ditches. In some examples, e.g., when the work machine 10 is a combine harvester, the control 202, 302 can determine crop conditions such as plant height, plant condition, plant stand, plant type, biomass yield, mechanics, weed presence, weed extent (e.g., size, quantity, and / or material properties of the weeds), and / or weed type. In some examples, the control 202, 302 can determine that a condition of plants in a farm field can alternatively or additionally affect the work machine.In some examples, the control 202, 302 can determine that the working machine 10 can be influenced in one or more ways by interaction with a plant, which may be standing, lying, partially lying, inclined, harvested and / or uniform / uneven crop.

[0011] In some examples, the controller 202, 302 can determine the performance of the working machine, at least partially, based on various performance attributes. In some examples, the controller 202, 302 can determine the feed rate, machine speed, stubble height and uniformity (e.g., deviations and / or drooping), material flow including the feeding and / or collection of material within the working machine 10, material convergence within the working machine 10, unthreshed crop that was engaged with the working machine 10 but was lost, missed, or ejected, length of processed crop, vibrations, distance traveled by the working machine, or other aspects related to the working machine's performance. In some examples, the controller 202, 302 can determine various machine attributes of the working machine 10 that may indicate undesirable conditions.For example, the control 202, 302 can determine a course of the working machine 10, a target speed, a header height and / or orientation (e.g. lateral tilt, forward / backward tilt), a screw / belt speed, a reel height, a forward / backward position of the reel, a reel speed and / or a reel finger timing, an upper screw position and / or speed, the speed of the rear shaft and / or a height and / or a finger timing of the feed drum / screw.

[0012] In some examples, such as those relating to the determination of insufficient crop input, the control unit 202, 302 can determine, alternatively or in addition to the aspects described above, one or more aspects that may contribute to an undesirable condition of the working machine 10. For example, the control unit 202, 302 can determine precipitation conditions such as ice, snow, and / or liquid, and further detect and determine that these contribute to an undesirable operating condition.

[0013] In some examples, such as those relating to draper belt tear detection, the controller 202, 302 can determine one or more aspects that may contribute to an undesirable condition for the working machine 10, either alternatively or in addition to the aspects described above. For example, light intensity and / or angle (e.g., day, night, quantity / intensity, direction, and / or angle of light distributed across an operating environment) can contribute to draper belt wear and / or impede the detection of draper belt conditions. The controller 202, 302 can also determine performance aspects such as ambient temperatures, surface temperatures, and internal temperatures (e.g., battery cells, cabin, etc.) in relation to the draper belt. Furthermore, the controller 202, 302 can determine the expected durability and / or service life of components such as the belt and filters (e.g.,wear resistance, wear, expected service life, etc.) are determined at least partially based on recorded data and / or predetermined data.

[0014] In some examples, such as those relating to the detection of undesired header folding conditions, the controller 202, 302 can determine one or more aspects that may contribute to an undesired operating condition, either alternatively or in addition to the aspects described above. For example, the controller 202, 302 can determine an auger position (e.g., whether in an extended or retracted position) and / or the percentage by which the auger has been extended. The controller 202, 302 can also determine an on / off state of the underloader. Furthermore, the controller 202, 302 can determine machine aspects such as mass and physical dimensions, as well as the function and / or mode of the working machine 10 and / or components of the working machine 10. Finally, the controller 202, 302 can determine whether sections of the header of the working machine 10 are switched on, switched off, lowered, or deactivated (e.g.,Lighting of the entire vehicle, the harvesting attachment, etc.) and whether sections of the work machine 10 are folded and / or unfolded. In some examples, the operation of the work machine 10 can be carried out by an operator in the operator's cab 16, a remote operator, or an automated system.

[0015] A cutting head 18 is arranged at a front end of the working machine 10 and is configured to harvest crop and convey the harvested crop to an inclined conveyor 20. The term "crop" as used herein includes grain (e.g., corn, wheat, soybeans, rice, oats) and non-grain components (NPCs). The cutting head 18 can be an auger platform, a belt cutter, a belt pick-up assembly, a corn header, a cutting platform with a reel assembly, or any other cutting head configured for harvesting crop at a point of operation. In some examples involving a belt cutter, the inclined conveyor 20 conveys the crop, after receiving the harvested crop from the cutting head 18, to a guide drum 22. The guide drum 22 directs the crop to an inlet 24 of a threshing assembly 26, as shown in Fig. Figure 1 shows that various subsystems of the working machine 10, such as the threshing arrangement, a device for conveying clean crop 28, a device for conveying crop residues 60 and a residue arrangement 82, work together to process the harvested crop.

[0016] The threshing assembly 26 comprises a housing 34 and one or more threshing rotors. Fig. Figure 1 shows a single threshing rotor 36. The threshing rotor 36 comprises a drum 38 arranged along a threshing axis 100, and the threshing rotor 36 rotates about the threshing axis 100. The threshing assembly 26 further comprises a feeding section 40, a threshing section 42, and a separating section 44. The feeding section 40 is arranged at a front end of the threshing assembly 26, the separating section 44 is arranged at a rear end of the threshing assembly 26, and the threshing section 42 is arranged between the feeding section 40 and the separating section 44. The threshing arrangement 26 further comprises a threshing concave 43, which is positioned in the threshing section 42 and below the threshing rotor 36, guide vanes 47, which are positioned above the threshing rotor 36, and a separating grate 45, which is positioned in the separating section 44 and below the threshing rotor 36.In the illustrated example, the guide vanes 47 direct the harvested crop backwards through the threshing assembly 26, and the crop is separated and spreads out when it comes into contact with the guide vanes 47. Crop falls through the threshing concave 43 and through the separating grate 45.

[0017] The harvested crop can be conveyed to the conveying assembly 28 for clean crops, which includes a blower 46 and sieves 48 and 50 with lamellae. The sieves 48 and 50 can oscillate axially. The conveying assembly 28 for clean crops removes NGB (non-Gross Grain) and conveys the grain via a screw conveyor 52 to a grain elevator 94. The grain elevator 94 deposits the grain into a grain tank 30, as shown in [reference to figure]. Fig. Figure 1 illustrates this. In some examples, the working machine 10 includes a sensor 230, which is positioned, for example, on the grain elevator 94 and configured to measure the grain yield of the harvested crop. In this example, the yield sensor 230 measures the force of the grain striking the sensor 230 to determine the yield. The grain in the grain tank 30 can be unloaded, for example, onto a grain wagon, trailer, or truck by a discharge screw conveyor 32.

[0018] Crop remaining at the rear end of the sieve 50 is transported again to the threshing unit 26 by a screw conveyor 54, where it is processed a second time. Crop remaining at the rear end of the sieve 48 is conveyed by an oscillating plate conveyor 56 to a lower inlet 58 of a crop residue conveying unit 60. Crop at the threshing unit 26 is processed by the separating section 44, which separates the straw from other crop material. The straw is discharged via an outlet 62 of the threshing unit 26 and directed to a discharge drum 64. The discharge drum 64 interacts with a plate 66 located below it to move the straw backward. A wall 68 is located behind the discharge drum 64 and guides the straw into an upper inlet 70 of the crop residue conveying arrangement 60.In the crop residue conveying arrangement 60, blades of a rotary chopper work together with knives to chop the straw into smaller crop residues.

[0019] Residues of the harvested crop are moved from the crop residue conveying unit 60 to the residue unit 82 for optional further processing and ejection from the working machine 10. As in Fig. As shown in Figure 1, the residue arrangement comprises, for example, one or more spreaders provided downstream of an outlet 80 of the crop residue conveying arrangement 60. One spreader 84 is located in Fig. Figure 1 shows that the chopped straw is distributed as it exits the working machine 10 by rotating the spreader 84's blades about an axis 88. The working machine 10 can include one or more sensors 210, 212, 214, such as a camera, which can be positioned on the working machine (e.g., at the top, bottom, front, or rear end of the working machine 10) and configured to capture one or more images of a field of view (FOV) arranged around the working machine. The FOV can include one or more sections of an agricultural field and / or sections of the working machine (e.g., the header).

[0020] In some examples, the one or more sensors of the working machine 10 include one or more image sensors configured to capture one or more images of the field of view (FOV) outside the working machine 10 (e.g., sections of the working machine and / or the field around the working machine). Fig. 1. The one or more image sensors 210, 212, 214 can be a camera. In some examples, however, the one or more image sensors 210, 212, 214 may alternatively or additionally be various sensors (e.g., optical or visual radiation cameras or red-green-blue (RGB) cameras), LiDAR sensors, radar sensors (e.g., long-range terahertz radar, mm-wave radar, ultra-wideband radar, frequency-modulated continuous wave radar (FMCW), ground-penetrating radar), ultrasonic sensors, thermal sensors (e.g., thermal imaging cameras), stereo cameras, laser vibrometers, infrared nuclear magnetic resonance cameras (NMR cameras), short-wave infrared cameras (SWIR cameras), infrared terahertz sensors, or other sensors that can capture or generate one or more images or data corresponding to the one or more images of the field of view. It is understood that, even with reference to Fig. 1 an exemplary working machine 10 is described, aspects of the disclosure (e.g. the control system and the procedures therein) are applicable to various working machines, such as agricultural machines configured for harvesting crops.

[0021] In Fig. Figure 2 shows an exemplary control system 200. The control system 200 comprises one or more memories 208 contained in or accessible to the controller 202, and one or more processors 206 contained in or accessible to the controller 202. The control system can be a central control system with which various functions of the working machine 10 can be implemented. For example, the control system 200 can at least partially implement one or more of the functions listed below. Fig. The processes described in sections 6 to 9 are implemented. The one or more processors 206 are configured to execute instructions (e.g., one or more algorithms) stored in the one or more memories 208. The controller 202 can be a single controller or a plurality of controllers operationally coupled. The controller 202 can be located at the working machine 10 or remotely from the working machine 10. In some examples, the control system 200 can operate independently or in conjunction with other control systems, such as an AI-based control system (see, e.g., Fig. 3).

[0022] The controller 202 can be connected to other components of the machine 10 and to one or more remote devices via a wired connection or wirelessly. In some cases, the controller 202 can be connected wirelessly to other components of the machine 10 and to one or more remote devices via Wi-Fi, Bluetooth, near-field communication, or another wireless communication protocol.

[0023] In the illustrative example in Fig. 2. The controller 202 is operationally coupled with at least one sensor 204, which is connected to at least one sensor 210, 212, 214, or 230. In the illustrative example, the controller 202 is configured to receive data corresponding to one or more images of the field of view (e.g., FOV) from one or more image sensors 210, and data corresponding to one or more images of the additional fields of view from image sensors 212 and 214.

[0024] Further according to Fig. In some examples, the controller 202 is operationally coupled to a display, e.g., the display 222 of the user interface 220, and configured to send one or more signals to the display based on a determination of a quantity of grain shown in one or more images of the field of view. In the example shown, the controller 202 is operationally coupled to the user interface 220 and configured to receive one or more signals from the user interface 220, which are inputs for operating characteristics of the working machine 10 (e.g.,The operating characteristics of the working machine 10 may include the speed of the working machine 10, the direction of travel of the working machine 10, the operating characteristics of the cutter head 18, the operating characteristics of the threshing assembly 26, the operating characteristics of the clean crop conveying device 28, the operating characteristics of the crop residue conveying assembly 60, and the operating characteristics of the residue assembly 82. Operating characteristics of the cutter head 18 include a cutter head angle along its axial extent, a cutter head height relative to the ground, a cutter head speed, a reel speed, a reel position, a reel tine angle, a corn header-cover plate distance, a draper belt speed, a cutter head downward force, and a cutter head lateral tilt. Operating characteristics of the threshing assembly 26 include a threshing rotor speed, a concave position relative to the threshing rotor, and a guide vane orientation.Operating characteristics of the conveying assembly 28 for clean crop include a blower speed and a sieve position. Operating characteristics of the crop residue conveying assembly 60 include a chopper speed and a position of the knives relative to the chopper. Operating characteristics of the residue assembly 82 include a control speed and a spreader orientation.

[0025] In some examples, the operating characteristics of the machine 10 are entered by a user via the user interface 220 based on the displayed determination of the grain quantity shown in one or more images of the viewing area. In other examples, the controller 202 is configured to automatically adjust one or more of the operating characteristics of the machine 10 (e.g., based on a determination of a grain quantity shown in one or more images of the viewing area, and without instruction from the user interface 220).

[0026] Further according to Fig. In some examples, the control unit 202 is operationally coupled with at least one header actuator 224, at least one soil engagement mechanism actuator 226, or both. The at least one header actuator 224 and the at least one soil engagement mechanism actuator 226 can each comprise at least one control valve, motor, linear (e.g., cylindrical) actuator, rotary actuator, or other actuator configured to adjust an operating characteristic of the working machine 10. As in Fig. As shown in Figure 3, in some examples the controller 202 is operationally coupled to a motor 228 of the working machine 10 and configured to send one or more signals to the motor 228 to adjust the speed of the working machine 10. In some examples the controller 202 is coupled to one or more additional subsystem actuators 225 (e.g. control valves, motors, linear actuators, rotary actuators) which are configured to adjust one or more operating characteristics of the threshing assembly 26, the clean crop conveying assembly 28, the crop residue conveying assembly 60 and the residue assembly 82.

[0027] In some examples, the control system 200 may include an offboard / offboard condition detection system 300 and / or be communicatively coupled with it. Fig. Figure 3 shows an architecture for an onboard / offboard condition detection system 300, which includes a controller 302 with a processor 304 and a storage device 306 (which may be identical to, alternative to, or additional to the controller 202). The onboard / offboard condition detection system 300 may include an AI (Artificial Intelligence) engine 308, which may include a neural network 310. Information collected from multiple agricultural vehicles can be used in the onboard / offboard condition detection system 300 to train and retrain one or more machine learning models, including algorithms, of the neural network 310. According to certain examples, the machine learning model(s) and optional updates of such models can be transmitted to the controller 202 of the agricultural vehicle 10 and stored in the agricultural vehicle 10, including in the storage device 208.Alternatively or additionally, the AI ​​engine 308 and the neural network 310 can be located on the working machine 10 and be communicatively coupled with the control 202.

[0028] The Neural Network 310 can utilize machine learning models, including algorithms, designed to improve the precision and efficiency of undesirable machine condition detection. As explained below, such machine learning models can be used to determine and / or customize one or more undesirable condition detections. Furthermore, various detection methods and / or routines can be determined using the machine learning model(s). Alternatively or additionally, the various detection methods / routines can be based on operator preferences or derived by the machine learning model(s) taking operator preferences into account. According to some examples, such machine conditions can correspond to default settings.

[0029] According to certain examples, the architecture of neural network 310 can include one or more input layers capable of processing raw information, including data, from one or more sensors 204, 210, 212, 214, 230, and identified operator preferences. Neural network 310 can further include multiple hidden layers of interconnected neurons capable of processing input information, such as input data, including the application of nonlinear transformations to extract complex patterns and relationships within the information. For example, the hidden layers can use activation functions to introduce nonlinearity and enhance neural network 310's ability to model intricate dependencies.The neural network 310 can further include an output layer that can generate information for controlling and / or adjusting one or more of the position, orientation, and speed of the working machine 10. The output layer can also generate information for controlling various other functions of the working machine, e.g., for actuating various components of the working machine (e.g., actuators).

[0030] The machine learning model(s) of the neural network 310 can be trained and / or retrained in various ways, e.g., through supervised learning, adaptive learning, and / or generative models. With regard to supervised learning, historical information stored in a historical database 312 can, for example, include tagged examples of past agricultural operations where at least an attempt was made to successfully perform an agricultural function, and optimization models, such as gradient descent or Adam optimizers, can be used to minimize the error between predicted and actual results. With regard to adaptive learning, the neural network 310 can, for example, continuously learn adaptively from information that is available at least in near real-time, e.g.,from information provided by one or more sensors 204, 210, 212, 214, 230, and refine the accuracy or efficiency of the machine learning model by updating applied weights based on feedback information. With regard to generative machine learning models, the neural network 310, for example, can simulate various agricultural operations based on existing data to predict potential difficulties and develop strategies to mitigate these difficulties.

[0031] The onboard / offboard condition detection system 300 can include a variety of databases 312, 314, 316, 318 that can store a variety of different types of historical, operator preference, and / or identification information that can be used when training or retraining the machine learning model(s) of the neural network 310. For example, the onboard / offboard condition detection system 300 can include a historical database 312 that can store information about previous commands generated by one or more machine learning models of the neural network 310 in connection with previous agricultural operations. For example, the historical database 312 can include information about previous commands that involved determining and / or handling undesired working machine conditions.Thus, the historical database 312 can include, among other types of information, records of previous specific adjustments to the speed, orientation and / or course of the working machine 10, as well as other parameters, which were made upon detection of one or more undesirable conditions based on one or more determinations output by the machine learning model(s) of the neural network 310.

[0032] The historical database 312 can also include feedback information relating to previous agricultural operations in which one or more provisions based on an output of the machine learning model of the neural network 310 were used. Such feedback information can include, for example, information received from one or more sensors 204, 210, 212, 214, 230 during previous agricultural operations, one or more detections of a blocking fault condition, detection of crop loss in a machine operation, detection of draper band breaks in a machine 10, detection of a header misalignment, and detection of a header unfolding in a machine 10, which can be provided by one or more of a variety of sensors that can be communicatively coupled to the machine 10.The feedback information may also include adjustments made by the operator or other systems to one or more operations of the working machine 10 and based on information from the machine learning model of the neural network 310, as well as other possible variables that may be present in connection with these operator-initiated adjustments, including, for example, terrain information, crop information and / or machine condition information that may be displayed by a variety of sensors.

[0033] The feedback information stored in historical database 312 may also include performance indicators that may provide an indication of the success or lack thereof in agricultural operations, including data relating to machine condition conditions, terrain conditions and / or crop conditions.

[0034] The historical database 312 can also include additional information that may influence the working machine 10 during an agricultural operation. Such additional information may include, for example, information about environmental conditions during past operations, such as soil moisture content and / or rainfall amounts, which may affect the interaction of the working machine 10 with the soil. This, in turn, may affect performance or operation, such as influencing the turning, stopping, and / or speed adjustments of the working machine 10. In some examples, additional information may include information about machine conditions that may affect the operation of the working machine 10, such as the conditions of one or more parts of the working machine 10 during different stages of the process.

[0035] The information provided by the historical database 312 can enable the neural network 310 to utilize agricultural operations to optimize future agricultural operations, including optimizing driving parameters associated with different distance thresholds. By analyzing patterns identified by the neural network 310, at least from the information stored in the historical database 312, the neural network 310 can refine the machine learning model(s) to increase prediction accuracy and improve the efficiency of agricultural machinery operations, also with respect to the machine operating and field parameters obtained by using the machine learning model(s) for different driving conditions, as described above.Furthermore, the historical database 312 can support the adaptive learning described above by enabling the neural network 310 to update its models in real time based on information collected, for example, from one or more sensors 204, 210, 212, 214, 230 and / or from operator input via the user interface 220 during operation. Such a continuous learning process can help adapt the detection and adjustment of undesirable conditions by the agricultural machine 10 to different conditions and operator preferences during agricultural operations.

[0036] The onboard / offboard condition recognition system 300 can also include one or more databases, such as an operating condition database 314, which can contain various information about at least the working machine 10, among other working machines, that is to be involved in a current or upcoming agricultural operation. The specific working machine for which information stored in the operating condition database 314 is to be retrieved and / or used in connection with a working machine operation, including a current or upcoming working machine operation, can be identified in various ways.According to certain examples, the agricultural machine 10, which is or will be involved in the agricultural operation and for which information is to be retrieved, can be identified by an operator who enters one or more identifiers for the agricultural machine 10 via an input device (e.g., the user interface 220). Furthermore, according to certain examples, one or more of the sensors 204, 210, 212, 214, 230 can acquire information, including images, from which unique characteristics of the agricultural machine 10 and / or the field can be extracted. Such extracted information may include identification codes, symbols, or labels and / or involve the controller 302 analyzing a corresponding shape and / or size of the agricultural machine 10 or a section thereof from the acquired information.

[0037] The operating conditions database 314 can store a variety of information about operating conditions that can facilitate a work machine operation, such as an agricultural operation. For example, the operating conditions database 314, including an identification database 316, can store an identification of a work machine component type, such as a header type, and at least certain physical dimensions of the work machine 10. In some examples, the identification database can store identification information for crop types as well as for non-crop objects that may be located in an agricultural field.

[0038] The operating condition 314, including a location database 318, can store information about the recorded location of various working machine conditions and / or field conditions. Furthermore, the location database 318 can include, among other location information, coordinates (e.g., latitude and longitude) of the working machine 10 or sections thereof. This information can also be helpful in determining operating parameters to achieve a working machine operation, such as an agricultural operation. While the information discussed above can be stored in the operating condition database 314, including the identification and location databases 316 and 318, such or similar information can also be stored in the storage device 208.

[0039] Fig. Figure 4 shows an example of the working machine 10 during an agricultural operation. The working machine 10 includes a variety of sensors 402 for operation and / or automation of the working machine. The variety of sensors can be one or more sensors attached to the working machine and / or external sensors (e.g., drone, satellite, etc.). The sensors 402 can be the same or similar sensors as those used in Fig. 2 are described (e.g., sensors 210, 212, 214). The working machine 10 can use one or more of the sensors before, during, and / or after the operation. For example, the working machine 10 can use one or more of the sensors 402 to determine grain harvest quality, terrain, or position relative to crop or non-crop objects. In some examples, the working machine 10 can alternatively or additionally use one or more of the sensors 402 to determine various aspects related to the operation and / or a condition of the working machine 10. During a working machine operation, the working machine 10 can be oriented in a desired direction of travel. The working machine 10 can include onboard sensors configured to determine a working machine condition and a condition of the agricultural field with respect to different coordinate systems, such as the direction of travel.

[0040] In some examples, one or more sensors 402 of the working machine 10 can be configured to monitor and / or acquire one or more FOVs 404 arranged around desired sections of the working machine. The desired FOV(s) 404 can be one or more sections of a field or of the working machine that can be monitored for data relating to the working machine operation. For example, the one or more sensors 402 can be configured to provide optical data about sections of an area around a desired section of the working machine (e.g., behind the header or immediately next to / under the combine harvester).

[0041] During the operation of a machine, various conditions may arise in which a user may wish to detect and / or respond to one or more of these conditions. For example, the machine and / or a section of the machine may enter a fault condition such that one or more functions of the header do not operate as intended (e.g., blocked, clogged, etc.). In some examples, crop may be separated during certain harvesting operations but not picked up by the combine harvester. In some examples where the machine is a combine harvester, the combine may include a draper belt. In some of these examples, a condition of the draper belt may transition into an undesirable state before, during, or after operation (e.g., cracks, stretching, misalignment, holes, burns, deformations).

[0042] In some cases, the operation of an agricultural combine harvester can lead to misalignment of the combine with the crop. For example, the harvesting width or row spacing of the combine may not match the width or row spacing of the crop planted with a seed drill. These discrepancies can cause the combine to miss a portion of the crop during a single pass in an agricultural operation. Furthermore, issues may arise regarding the stowing and unfolding of sections of the combine. In some cases, the machine may not fold or unfold a header in an undesirable manner or at all.In some of the examples described above, various methods and systems can be used to detect such conditions and adjust the operation of the working machine 10 so that it operates as desired. In some examples, various aspects related to the performance of the working machine 10 can be detected to determine different conditions, such as undesired header conditions, uncollected crop, draper band breaks, misalignment during harvesting, and / or undesired header folding and / or unfolding conditions.

[0043] In some examples, the controller 202, 302 can determine various machine characteristics and environmental attributes that may contribute to undesirable machine conditions. The controller 202, 302 can determine the machine characteristics and / or environmental attributes, at least partially, based on sensor data relating to the machine 10. In some examples, the controller 202, 302 can determine various factors such as environmental conditions, the position of the machine 10, the terrain in which the machine is operating (e.g., slope, forward tilt, lateral tilt, etc.), soil firmness, and / or environmental attributes (e.g., dew point and / or humidity). In some examples, the controller 202, 302 can determine the presence of essentially fixed field features such as a field boundary, a field road, a ditch, a watercourse, and / or a terrace.In some examples, the control 202, 302 can essentially determine movable, non-crop objects such as rocks, flagstones, power poles, tree trunks, other machinery, and / or irrigation ditches. In some examples, e.g., when the work machine 10 is a combine harvester, the control 202, 302 can determine crop conditions such as plant height, plant condition, plant stand, plant type, biomass yield, mechanics, weed presence, weed extent (e.g., size, quantity, and / or material properties of the weeds), and / or weed type. In some examples, the control 202, 302 can determine that a condition of plants in a farm field can alternatively or additionally affect the work machine.In some examples, the control 202, 302 can determine that the working machine 10 can be influenced in one or more ways by interaction with a plant, which may be standing, lying, partially lying, inclined, harvested and / or uniform / uneven crop.

[0044] In some examples, the controller 202, 302 can determine the performance of the working machine, at least partially, based on various performance attributes. In some examples, the controller 202, 302 can determine the feed rate, machine speed, stubble height and uniformity (e.g., deviations and / or drooping), material flow including the feeding and / or collection of material within the working machine 10, material convergence within the working machine 10, unthreshed crop that was engaged with the working machine 10 but was lost, missed, or ejected, length of processed crop, vibrations, distance traveled by the working machine, or other aspects related to the working machine's performance. In some examples, the controller 202, 302 can determine various machine attributes of the working machine 10 that may indicate undesirable conditions.For example, the control 202, 302 can determine a course of the working machine 10, a target speed, a header height and / or orientation (e.g. lateral tilt, forward / backward tilt), a screw / belt speed, a reel height, a forward / backward position of the reel, a reel speed and / or a reel finger timing, an upper screw position and / or speed, the speed of the rear shaft and / or a height and / or a finger timing of the feed drum / screw.

[0045] In some examples, such as those relating to the determination of insufficient crop input, the control unit 202, 302 can determine, alternatively or in addition to the aspects described above, one or more aspects that may contribute to an undesirable condition of the working machine 10. For example, the control unit 202, 302 can determine precipitation conditions such as ice, snow, and / or liquid, and further detect and determine that these contribute to an undesirable operating condition.

[0046] In some examples, such as those relating to draper belt tear detection, the controller 202, 302 can determine one or more aspects that may contribute to an undesirable condition for the working machine 10, either alternatively or in addition to the aspects described above. For example, light intensity and / or angle (e.g., day, night, quantity / intensity, direction, and / or angle of light distributed across an operating environment) can contribute to draper belt wear and / or impede the detection of draper belt conditions. The controller 202, 302 can also determine performance aspects such as ambient temperatures, surface temperatures, and internal temperatures (e.g., battery cells, cabin, etc.) in relation to the draper belt. Furthermore, the controller 202, 302 can determine the expected durability and / or service life of components such as the belt and filters (e.g.,wear resistance, wear, expected service life, etc.) are determined at least partially based on recorded data and / or predetermined data.

[0047] In some examples, such as those relating to the detection of undesired header folding conditions, the controller 202, 302 can determine one or more aspects that may contribute to an undesired operating condition, either alternatively or in addition to the aspects described above. For example, the controller 202, 302 can determine an auger position (e.g., whether in an extended or retracted position) and / or the percentage by which the auger has been extended. The controller 202, 302 can also determine an on / off state of the underloader. Furthermore, the controller 202, 302 can determine machine aspects such as mass and physical dimensions, as well as the function and / or mode of the working machine 10 and / or components of the working machine 10. Finally, the controller 202, 302 can determine whether sections of the header of the working machine 10 are switched on, switched off, lowered, or deactivated (e.g.,Lighting of the entire vehicle, the harvesting attachment, etc.) and whether sections of the working machine 10 are folded in and / or unfolded.

[0048] In some examples, one or more of the conditions described above can be at least partially addressed by a user who can assess and adjust aspects of the working machine 10 to make it operate in a desired manner. However, in some examples, such as autonomous working machines, conditions may not be directly addressed by a user. For instance, a user may not have direct access to the working machine to identify or adjust aspects of the working machine 10 so that it operates in a desired way. Therefore, methods and systems for detecting aspects of working machine operation and for adjusting the working machine operation in response to the detected aspects can be advantageous.This document presents methods and systems for detecting undesirable conditions associated with a working machine and / or a working machine process.

[0049] In some examples, the machine may be subject to undesirable blocking conditions (e.g., slow speed or blocking events). In such examples, the machine may be slowed down or blocked for one or more reasons, such as unwanted crop intake, ingestion of non-crop objects, undesirable mechanical conditions, and / or obstacles in the terrain. In some examples, one or more components of the machine may experience undesirable blocking conditions that impair the machine's operation. Examples of machine components that may experience undesirable blocking conditions include the reel, the material feed screw(s), the mid-feed area, the cutting unit, the stalk rollers, the gathering chains, the end guards, or other components relevant to the machine's operation.For example, a combine harvester's reel can become blocked, and augers of a combine harvester can become clogged. The working machine 10 can detect such blockage faults using one or more sensors configured to identify blockage states and / or causes (e.g., sensors 402). The controller 202, 302 can further be configured to provide one or more actions and / or instructions for handling the one or more blockage states.

[0050] Fig. Figure 5 is a flowchart 500 that shows example steps 502 to 508 for detecting a blocking fault condition in a working machine. The procedure can be implemented by a system for monitoring a section of the working machine 10 and / or the agricultural field, which may include one or more controllers such as one or more of the controllers 202, 302.

[0051] In step 502, the controller 202 determines one or more basic operating states. These basic operating states can specify one or more desired states for one or more parameters related to the operation of the machine. For example, the basic operating states can include a range of movement speed data indicating desired operating conditions. The basic operating states can also include ranges of one or more other operating aspects of the machine, such as the rotational speed of rotating components (e.g., reel, auger, etc.), the speed of reciprocating components (e.g., the back-and-forth motion of a cutter bar, etc.), or other ranges of motion rates of other moving components of the machine.One or more basic operating states can be based on one or more definitions of basic operating states for the machine 10, which are obtained from historical data and / or measurement data. For example, the basic operating states can be based, at least in part, on one or more data sets (e.g., databases) containing data such as image data or statistical operating data. In some examples, the data can be based, at least in part, on data from similar processes and / or processes in similar or identical regions and / or locations.

[0052] In step 504 of the procedure, the controller 202 receives speed indication data from one or more sensors. The speed indication data can include data about a movement speed or the operation of other machine components. The speed indication data can change when the speed of the working machine 10 changes. In some examples, the speed indication data is data that specifies an operating speed of a working machine 10, e.g., a combine harvester. The operating speed data can, for example, include movement speed data. In some examples, the speed indication data can include data relating to one or more machine components of the working machine 10, such as...a screw rotation, a back-and-forth movement of a cutter bar, or other operating data of the combine harvester and / or the combine harvester header, which may vary in relation to the travel speed of the combine harvester.

[0053] In step 506, the controller 202, 302 compares one or more basic operating states with the speed display data to determine a blocking fault condition. In some examples, the blocking fault condition may be related to an operational error during an agricultural operation. The blockage may be related to, for example, contact with the ground or objects, picking up non-crop objects, unintended crop picking, mechanical misalignment, and / or other conditions and / or events that can lead to undesired operation of the implement during an agricultural operation. For example, the controller 202 may compare a position change rate value recorded by a GPS with similar position data (e.g., the same field or geographic region).In some examples, the controller 202 can use one or more readings from onboard speed sensors to compare them to average speeds achieved during a similar operation. In some examples, the controller 202 can determine a reference speed based on onboard speed sensor readings using historical and / or current data.

[0054] In step 508, the controller 202 sends instructions to one or more systems related to the working machine to adjust one or more combine harvester operating parameters, at least partially, based on the blockage fault condition. The blockage fault condition may indicate errors in the combine harvester operation, such as a blockage, unwanted component function, or other aspects related to the combine operation that could lead to a blockage of the working machine 10. For example, the controller 202, 302 may send a warning to one or more operators so that they can make an operational adjustment to move the working machine 10 out of a blockage condition. In some examples, a user may remotely send instructions to change an operation of the working machine 10 (e.g., stopping or starting the system, accelerating or decelerating the working machine 10, moving a component, etc.).

[0055] The working machine 10 can also send instructions to components such as the motor or actuators of the working machine 10 to cause the working machine 10 to make adjustments to eliminate the blocked condition. For example, the controller 202, 302 can cause the working machine 10 to be activated or deactivated in order to move a harvesting header, stop the operation of the harvesting header, stop the rotation of a rotating assembly (e.g., reel, auger, belt, etc.), stop the movement of a reciprocating assembly (e.g., cutter bar), and / or start certain components of the working machine 10 to handle the fault condition.For example, if the controller 202 receives an indication, based on machine speed and auger speed data, that the working machine 10 is in a blocked state, the system can determine that the working machine 10 is blocked due to a clogged combine harvester. In such examples, the system can determine a combine harvester speed at which clogged parts are likely to dislodge and the working machine 10 can resume operation outside the fault condition. In some examples, the controller 202 can determine other corrective actions or combinations of actions with respect to the working machine 10 to resolve the fault conditions.

[0056] In some examples, the machine 10 may be subject to conditions that cause it to fail to harvest crops and / or to run over fallen crops without picking them up. In such examples, the machine 10 may run over fallen crops for various reasons, such as unwanted crop pickup, pickup of non-crop objects, undesirable mechanical conditions, and / or obstacles in the terrain. For example, the machine 10 may run over crops that were cut and picked up by the machine but were lost due to insufficient pickup and / or by being dropped and / or thrown from the header. In some of these examples, the material may run back under the draper belts of the header and / or the winding mechanism and be discharged below the header. In some examples, components of the header, such asEnd dividers are positioned to knock over or bend crop material, causing the work machine 10 to run over it. The work machine 10 can detect such uncollected crop material using one or more sensors configured to identify blockage conditions and / or causes. The controller 202, 302 can further be configured to provide one or more actions and / or instructions for handling the crop-run-over condition.

[0057] Fig. Figure 6 is a flowchart 600 showing exemplary steps 602 to 610 for detecting separated crop material that was not taken into the working machine 10. The procedure can be implemented by a system for monitoring a section of the working machine 10 and / or the agricultural field, which may include one or more controllers such as one or more of the controllers 202, 302.

[0058] In step 602, the controller 202, 302 receives movement speed data from one or more speed detection systems. For example, the controller 202, 302 can receive speed detection data from one or more speed detection systems. The controller 202, 302 can, for example, receive data from speed detection systems such as GPS data, which can indicate a rate of change of the working machine 10, for example, during an operation. Alternatively or additionally, the controller 202, 302 can receive data from an onboard speed detection system such as a speedometer, accelerometer, or other onboard sensor configured to determine a travel speed of the working machine 10.

[0059] In step 604, the controller 202, 302 receives crop condition data from one or more perception sensors, which can operate as input data sensors. The crop condition data can be, for example, image data from one or more optical sensors such as one or more cameras, lidar, radar, or other optical sensors like those described in this application. In some examples, the crop condition data can be obtained from one or more optical sensors, which may be arranged in one or more configurations relative to the working machine 10.The perception system may, for example, be located on one or more of the working machines, satellites, drones, vehicles in the vicinity and / or other detection locations, positioned in such a way as to provide image data relating to the working machine 10, the crop feed and / or surrounding field areas as described above (e.g. identified crop rows).

[0060] The control unit 202, 302 can also receive crop condition data from other displays related to the working machine 10. For example, the control unit 202, 302 can determine vibrations that indicate interaction between the crop lying on the ground and the working machine 10. The control unit 202, 302 can also receive data based on other operating conditions, such as the rotational speed of rotating components or the reciprocating motion of moving components.

[0061] In some examples, the crop condition data may include data that indicates crops in relation to an agricultural operation. For example, the crop condition data may indicate crops lying on the ground, crops not fed in, crops not harvested, or other crop conditions. The crop condition data may include crop conditions for crops at different locations relative to the farm machinery 10. For example, the crop condition for crops in front of, to the side of, and / or behind a farm machinery 10 may be determined relative to a direction of travel during a harvesting operation.

[0062] In step 606, the controller 202, 302 determines one or more crop condition states, at least partially, based on the movement speed data and / or the crop condition data. The crop condition states are states of crop conditions with respect to the driven machine 10 and / or an intended operation of the driven machine. For example, the controller 202, 302 can process the crop condition data and / or vibration data and determine whether there are anomalies in the data that might indicate that crop is lying but not being picked up. In some examples, the controller 202 can determine, based on image data, that crop is lying down while the vibration is within a desired range. The controller 202, 302 can determine that the crop is likely to be picked up and that the crop condition is such that the crop is lying down and will be picked up.In some examples, the control unit 202 can determine, at least partially based on image data, that crop is lying down while the vibration is in an undesirable range. The control unit can also determine, at least partially based on additional image data around the combine harvester (e.g., crop images behind the header), that crop is being left behind. The control units 202 and 302 can determine that the crop is not being picked up and that the condition of the crop is such that it is lying down and not being picked up.

[0063] In step 608, the controller 202 determines that crop material is located near the machine 10 and can therefore be lost in a lying, but not picked up, state. The controller 202 can determine the amount of lost crop material, at least partially, based on the crop condition state. The crop condition state can, at least partially, specify a rate at which the machine 10 runs over lying crop material, based on data about the crop material lying down and / or the machine's travel speed.

[0064] For example, the crop condition state may be based, at least in part, on indications of crop lying on the ground, such as vibrations of the working machine, interruption of pickup in rotating and / or reciprocating components, and / or anomalies in other components of the working machine 10 that may be disturbed or hindered by interaction with crop lying on the ground. In some examples, the controller 202 may determine a cause for unpicked crop, at least in part, based on the crop condition state. For example, the controller 202 may determine that the working machine 10 has an undesired header condition or another undesired condition with respect to one or more other moving parts of the system related to the crop lying on the ground when crop is lying but not being picked up.For example, the harvester header may operate at an undesirable cutting rate and / or rotational speed, causing the harvested crop to be dropped and / or thrown out of the harvester header.

[0065] In step 610, the controller 202 adjusts one or more combine harvester operating parameters, at least partially, based on the determination of crop loss. For example, the controller 202 can send a warning to one or more operators so that they can make an adjustment to one or more operations to cause the worker 10 to pick up crop. The worker can be operated in such a way that a user can make a remote or non-remote change to the system (e.g., stopping or starting the system, accelerating or decelerating the worker 10, moving a component, etc.). In some examples, the combine harvester operating parameters may include a warning message containing a mechanical change indicator, showing that a mechanical change might be required for a desired operation of the worker 10.In some examples, the warning may include certain types of mechanical changes that are based, at least in part, on crop condition data.

[0066] The controller 202 can also send instructions to the work machine 10 to adjust the operation of components such as the motor or actuators of the work machine 10, so that the work machine 10 adjusts its operation to avoid missing fallen crop. For example, the controller 202 can cause the work machine 10 to move, stop a movement, increase a movement speed, and / or decrease a movement speed. In some examples, the controller 202 can give instructions to move a header, stop a header operation, stop the rotation of a rotating arrangement, stop the movement of a reciprocating arrangement (e.g., the cutter bar), and / or stop and / or start certain components of the work machine 10 to handle the condition.For example, if the controller 202 determines that the working machine 10 is in a state where it is running over fallen crop due to a machine speed and the rotational speed of a screw combine, the controller 202 can determine that this running-over condition is due to a blocked combine. In such examples, the controller 202 can determine a combine rotational speed suitable for removing obstructing objects and causing the working machine 10 to resume operation outside the fault condition.

[0067] In some examples, the controller 202 can determine other corrective actions or combinations of actions with respect to the working machine 10 to ensure that the working machine 10 does not miss and / or run over lying crop. For example, the controller 202 can send instructions to adjust a header cutting height, a mechanical position of the header and / or header components (e.g., end dividers and / or crop flow / orientation points), a header cutting speed, and a header downward pressure and / or downward force. In some examples involving a belt, the instructions may include adjusting a belt speed.In some examples where separated crop is not picked up due to misalignment with crop rows, corrective measures may include adjusting the orientation of a header to align it at least partially with crop rows so that the header can pick up the crop.

[0068] In some examples where the machine 10 has one or more draper belts, the machine 10 may be exposed to conditions that cause undesirable draper belt conditions. In such examples, the draper belt may be slippery and / or exhibit cracks, deformations, and / or other undesirable conditions. Undesirable draper belt conditions can arise for various reasons, such as interaction with unwanted objects, heat, material usage, and / or other conditions that can affect the condition of a draper belt. The controller can determine undesirable draper belt conditions using data from one or more sensors configured to identify one or more draper belt conditions and / or their causes.The controller can also be configured to provide one or more actions and / or instructions to handle one or more draperband conditions.

[0069] Fig. Figure 7 is a flowchart 700 showing example steps 702 to 708 for detecting one or more undesirable draper belt conditions in the working machine 10. The procedure can be implemented by a system for monitoring a section of the working machine 10 and / or the agricultural field, which may include one or more controllers such as one or more of the controllers 202, 302.

[0070] In step 702, the controller 202 can receive belt operating data from one or more sensors. The belt operating data can be data indicating one or more aspects of the belt operation during a current state of the working machine 10 (e.g., during or between agricultural operations). The operating data can, for example, include optical data showing images of sections of the draper belt in a current state. In some examples, the optical data can include optical data for one or more sections of a draper belt as the sections of the draper belt pass an optical data acquisition device such as a camera, radar, lidar, or other optical data acquisition devices as described in this application. In some examples, the operating data can include data relating to other draper belt condition indicators.For example, the operating data can include data such as the rotation speed of the draper belt or vibration data of the draper belt.

[0071] In step 704, the controller receives belt condition basic data, at least partially, based on one or more sensors. The belt condition basic data includes data relating to one or more belt conditions in a desired state. For example, belt condition basic data may include image data showing images of sections of a draper belt in a desired state. The image data may, for instance, include images of one or more sections of a belt cutter in various wear states but under desirable operating conditions. In some examples, the image data may include data on surface conditions, including markings and coloring. In some examples, the basic data may include data relating to other belt cutter condition indicators.For example, the basic data can include data such as the rotation speed of the draper belt or vibration data of the draper belt.

[0072] In step 706, the controller 202 determines a belt active condition, at least partially, based on the belt baseline data and the belt operating data. In some examples, the active condition may be a condition that the controller 202 determines is desirable for continued operation in a current state. In other examples, the controller 202 may determine that the belt active condition is an undesirable condition. The controller 202 can compare the belt baseline data and the belt operating data to determine differences between them and identify the belt active condition. In some examples, the controller 202 may determine that one or more deviations between the belt baseline data and the belt operating data indicate one or more belt active states (e.g., cracks, growth, deformation, etc.).For example, the controller 202 can determine that one or more differences in coloration indicate an undesirable belt operating condition. In some examples, the controller 202 can determine that a visual indication of a crack or deformation may be an indication of an active condition.

[0073] The controller 202 can be configured to initiate a variety of routines for belt monitoring and identifying the active condition at a specific time and / or over a specific period. In some examples, the controller 202 can issue instructions to inspect the draper belt over a predetermined period. In some examples, the controller 202 can issue instructions for continuous inspection of the draper belt during one or more operations, such as harvesting. In some examples, the controller 202 can issue instructions to a user to manually inspect the belt. In some examples, the controller 202 can issue instructions to automatically execute an inspection routine. In some examples, the inspection routine can be performed during an agricultural operation.In some examples, the inspection routine can be carried out when the working machine 10 is located outside the harvesting areas (e.g. at the headland).

[0074] The controller 202 can be configured to trigger a routine for inspecting the belt in multiple configurations and at multiple locations to improve the inspection angle for the optical sensors. For example, the controller 202 can be configured to issue instructions to raise a harvesting header, slow the belt speed, and / or position components (e.g., the reel) in a non-obstructive position to ensure the desired belt exposure for one or more optical sensors.

[0075] In step 708, the controller 202 can determine a change in the active condition, at least partially, based on the tape operating data and the tape base data. The controller 202 can determine that the tape operating data has changed with respect to one or more specific aspects of the base data. In some examples, the controller 202 can determine, at least partially, based on the operating data and the tape base data, that there has been no change in the active condition. For example, the operating data may show a consistent aspect (e.g., color, surface structure, etc.) between one or more active condition data points and the base data over a period of time. In some examples, the operating data may show an inconsistent aspect (e.g., color, surface texture, etc.) between one or more active condition data points and the base data.

[0076] In step 710, the controller 202 can adjust one or more belt operating parameters, at least partially, based on a determination of the active condition change with respect to a draper belt. For example, the controller 202 can send a belt warning indicating a belt condition to one or more operators so that they can make an operational adjustment to handle the draper belt active condition change. The operation can be such that a user can make a remote or non-remote change to the system (e.g., stopping or starting the system, accelerating or decelerating the work machine 10, moving a component, etc.). The controller 202 can also send instructions to components such as the motor or actuators of the work machine 10 to cause the work machine 10 to make the changes to handle the active condition change.For example, the control 202 can cause the working machine 10 to reduce a draper belt speed and / or deactivate a belt, so that the working machine 10 stops the rotation of a draper belt.

[0077] The controller 202 can alternatively or additionally send instructions to stop and / or start specific components of the working machine 10 to handle the change in the active condition. For example, if the controller 202 determines a change in the active condition indicating that the draper belt is unsuitable for the desired further operation, the system can raise or lower the draper belt to cause it to disengage from potentially interfering surfaces and / or objects. In some examples, however, the controller 202 can also determine other corrective actions or combinations of actions relating to the working machine 10 to handle the change in the active condition.

[0078] In some examples, the machine 10 may be subject to one or more conditions that cause it to be misaligned with the crop during harvesting. In some examples, a crop may be planted with a specific number of rows (e.g., 10, 14, 16, 18, etc.). In some examples, the planted crop may be harvested by the machine 10. In such examples, the machine 10 may be a combine harvester whose header has a width that covers either more or fewer rows than the number of planted rows. In some of these examples, the combine harvester's header may be misaligned with the crop rows to harvest all of them, which can lead to undesirable harvesting conditions.In some examples, a section of a combine harvester may be non-operational, so the portion of a set of crop rows actually harvested is limited to the operational section of the header. In some examples, the controller 202, 302 may be configured to detect such crop misalignment.

[0079] Alternatively or additionally, in some examples, the controller may be configured to instruct the worker 10 to take corrective action. For example, the controller 202, 302 may be configured to warn / notify an operator on board and / or a remote supervisor and / or send instructions to one or more of the worker and / or worker components to address the crop misalignment. For example, the controller may transmit instructions to one or more parts of the worker 10 so that the worker 10 realigns itself with the crop rows and / or makes additional passes to address harvesting conditions. In some examples, the worker 10 may be configured to process and plan a desired route for the efficient harvesting of misaligned crop rows.

[0080] Fig. Figure 8 is a flowchart 800 showing example steps 802 to 810 for crop alignment in the working machine 10. The procedure can be implemented by a system for monitoring a section of the working machine 10 and / or the agricultural field, which may include one or more controllers such as one or more of the controllers 202, 302.

[0081] At 802, the controller receives machine position data from 202. The controller can receive data about the position of machine 10 in a field. The machine position data can include, for example, data relating to the machine angle and / or the header angle in relation to crop (e.g., a row of crop) during an agricultural operation. The machine position data can also refer to the machine's direction of travel during an agricultural operation. In some examples, the machine position data can include data about its lateral position in relation to crop.

[0082] The data can be received from one or more onboard sensors and / or one or more external sensors such as drone or satellite imaging. For example, a general work machine position can be identified by one or more satellite images, and the work machine position can further be determined relative to other objects in the field by onboard sensors such as cameras, lidar, radar, or other sensors as described in this application. In some examples, the work machine position data can be based at least partially on one or more data types. For example, the work machine position data can be based at least partially on optical data (e.g., image data) and / or map data such as field map data.

[0083] The 804 controller 202 can receive crop position data. This crop position data can be used to determine the location of crops in a field. The crop position data can originate from one or more onboard sensors and / or one or more external sensors, such as drone or satellite imagery. For example, the controller 202 can identify a crop position in a field where the worker 10 is operating, using one or more satellite images in combination with one or more sensors on the worker 10.

[0084] The crop position can also be determined relative to other objects in the field by onboard sensors such as cameras, lidar, radar, or other sensors, as described in this application. The crop position can include the position of any section of the crop that may be relevant to the working machine 10 for the purposes of a harvesting operation. For example, the crop position data can include the position of a small section of the crop. Alternatively or additionally, the crop position data can refer to crops related to the operation of the working machine 10. The crop position can also include data on whether the crop is lying down and / or being harvested while the working machine 10 passes over it. The crop position data can include data indicating that a section or all of the crop being passed in a harvest row is being harvested.

[0085] In step 806, the controller 202 determines one or more alignment relationships between the working machine 10 and the crop based on the position data of the working machine 10 and the crop position data. The controller 202 can relate the working machine position data and the crop position data to each other to determine where the working machine 10 is located relative to the crop. For example, the working machine 10 might be a combine harvester capable of harvesting a row of crop. The controller 202 can determine that the combine harvester is aligned with the planted crop row, or that a section of the crop row is aligned, and / or that only a section of the planted crop row is actively being harvested. For example, as described above, a crop row might be planted with a seed drill whose number of rows is greater than the width of a header used for harvesting.Thus, the control unit 202 can determine that the harvester header is not aligned with the crop row.

[0086] In some examples, a section of a combine harvester header may be non-functional due to an undesirable condition. For instance, the non-functional section of the header might not harvest any crop, and the functional sections of the header might not be aligned with the entire row being harvested. Therefore, the control unit 202 can determine an alignment relationship between the working machine 10 and the crop.

[0087] In step 808, the controller 202 determines a misalignment state, at least partially, based on the alignment relationship. The misalignment state can be a determination of the state of the machine 10 relative to the crop. For example, the misalignment state can be determined based on an intended alignment between the machine 10 and the crop. In some examples, the misalignment state can be determined as the machine 10 being misaligned due to an unintended alignment between the machine 10 and the harvested crop.

[0088] In step 810, the controller 202 can transmit instructions to change the orientation of one or more of the working machines 10 and / or working machine components (e.g., combine harvester header), at least partially, based on one or more alignment relationships and the alignment status. The controller 202 can adjust one or more operating parameters, at least partially, based on the determination of the alignment relationship. For example, the controller 202 can send a warning to one or more operators so that they can adjust the operation to address a misalignment determination. The user can make a remote or non-remote change to the system (e.g., stopping or starting the system, accelerating or decelerating the working machine 10, moving a component, etc.).The controller 202 can also send instructions to components such as the motor or actuators of the working machine 10 to cause the working machine 10 to make the changes necessary to handle the change in the active condition. For example, the controller 202 can cause the working machine 10 to steer in a desired direction to align the working machine 10 with a desired section of the crop.

[0089] The control unit 202 can issue instructions for changing the lateral position and / or heading of the working machine 10 with respect to a direction of travel of the working machine 10 and / or with respect to a crop row. In some examples, the control unit 202 can issue instructions to move the working machine 10 into a desired field position. For example, the control unit 202 can issue steering instructions to move the working machine 10 into a position where a section of a row can be harvested that is wider than the header. Thus, the control unit 202 can issue instructions for positioning the working machine 10 to harvest the unharvested section of that row.

[0090] In some examples, the control unit 202 can issue instructions for aligning the working machine 10 with a row of crop. For instance, the control unit 202 can issue alignment instructions so that a desired section of a header harvests. Thus, a functioning section of a header can be realigned to harvest the crop, while a non-functional section of the header can be aligned to bypass the row of crop.

[0091] In some examples, the working machine 10 can align itself with a row of crop so that a section of the row wider than the width of the working machine 10's header can be harvested by the working machine 10's header. The instructions can also be configured for the working machine 10 to make an additional pass to align a functioning section of a working machine header with missed crop because a non-functional section of the header is missing the crop. In some examples, the controller 202 can issue instructions for an additional pass to align a header with a section of missed crop that was missed on a single pass due to a larger planting area than the area covered by a combine harvester.

[0092] The controller 202 can alternatively or additionally provide instructions to stop and / or start specific components of the working machine 10 to handle the change in the active condition. For example, if the controller 202 determines a misalignment condition indicating an undesired misalignment, the controller can cause the working machine 10 to stop operation so that a user can manually intervene to adjust the working machine 10 to a desired state and / or orientation.

[0093] In some examples, the work machine 10 with a folding header can be configured to fold and unfold the header under desired conditions. In some examples, the desired conditions might be one or more desired times and / or one or more desired locations. In such examples, it can be advantageous to identify and verify a folded state of the header to determine whether the header is in the desired state when intended—for example, fully unfolded and in the operational configuration for harvesting. The work machine 10 can detect undesired stowed or unfolded conditions. The controller 202 can further be configured to provide one or more actions and / or instructions to handle the header condition (for example, stowing or unfolding the header as desired).

[0094] Fig. Figure 9 is a flowchart 900 showing example steps 902 to 908 for detecting a header deployment on the working machine 10. The procedure can be implemented by a system for monitoring a section of the working machine 10 and / or the agricultural field, which may include one or more controllers such as one or more of the controllers 202, 302.

[0095] In step 902, the controller 202 receives one or more header deployment indications from one or more header deployment sensors. These indications can be data showing the header deployment status, such as an extended or stowed state. The header deployment data can include, for example, one or more optical and / or image data, switch / circuit activation data, or other indications of the deployment or stowage of the working machine 10, including the header (e.g., actuator position, position detection, or component orientation sensors).

[0096] In some examples, the controller 202 can determine that one or more objects (e.g., non-crop objects) may be present in an area where the header can be deployed. In such examples, the controller 202 can be configured to detect the one or more non-crop objects based on one or more sensor readings. In some of these examples, the controller can determine one or more types of detected objects. In some examples, the determination of the one or more detected objects can be based on one or more attributes (e.g., object shape, size, orientation, location, proximity / distance, quantity, specific object identification, etc.). In some examples, the controller 202 can determine corrective actions to prevent the header from interacting with one or more non-crop objects.

[0097] In some examples, a combination of data sources can be used to determine a stowed or unfolded state. For instance, the work machine 10 can activate one or more internal switches to indicate when the header is in a stowed position. One or more optical sensors can provide optical data that, in conjunction with the switch indication, can be used to confirm the header's state (e.g., unfolded or stowed).

[0098] In step 904, the controller 202 can determine a desired header deployment state based on one or more indications of a desired header deployment state. An intended deployment state can be indicated, for example, by a user selection in a menu, by a predetermined time for deployment or stowing, and / or by an intended stowing and deployment position and / or location. In examples relating to an automated work machine 10 and / or a semi-automated work machine 10, the controller 202 can, for example, determine that the work machine 10 enters an area of ​​a field where there is an intention to harvest crops.

[0099] The control system 202 can determine that this location represents an intended deployment position. In some examples, the control system 202 can determine that the machine 10 is leaving a section of a field where it is to harvest crops. In such examples, the control system 202 can determine that the header should be stowed. In some examples, the control system 202 can determine that the machine 10 will soon be loaded onto a transport vehicle. The control system 202 can further determine that the header should be stowed. In other examples, the machine 10 may be unloaded from a transport vehicle, so the control system 202 can determine that the header should be deployed.

[0100] In some examples, the desired header deployment state can be based, at least in part, on the presence of an object, such as a non-crop object, in an obstruction path with the header. For instance, the controller can determine that a non-crop object is located in an area into which the header can deploy, and / or determine that an object can obstruct the header's movement path during stowing. In some examples, the controller can determine the desired header deployment state, at least in part, based on the attributes of the detected object that has been determined to obstruct the header's movement path (e.g., size, orientation, position, proximity / distance, quantity, specific object identification, etc.).

[0101] In step 906, the working machine 10 can determine one or more desired header deployment adjustments, at least partially, based on the one or more header deployment indicators and the one or more desired header deployment states. In some examples, the controller 202 may determine that the header should be deployed but is not in a deployed state. In such examples, the controller 202 may determine that a desired corrective action is to deploy the header. In other examples, the controller 202 may determine that the header is in a deployed state but should not be deployed. In such examples, the desired corrective action is to retract the header.

[0102] In some examples, the desired header deployment adjustments can be based on one or more sensor readings. For instance, in some examples, the controller 202 can determine header deployment adjustments, at least partially, based on a switch value that does not correspond to a value from an optical display. Thus, the controller 202 can also determine the intended position based on other factors such as position, operating time, and / or user input.

[0103] In step 908, the controller 202 initiates a header deployment correction action. This action can include a warning and / or an automatic action. In some examples, the correction might involve enabling and / or disabling one or more header functions. For instance, the correction might include disabling header component movements, such as reel movement, cutterbar movement, and / or belt movement. In some examples, the header deployment correction might involve deploying or stowing the header. For example, the controller 202 might determine that the header is deployed or stowed if it is not in its intended deployed or stowed state.

[0104] The controller 202 can provide instructions for activating one or more header closing mechanisms and / or one or more header extension mechanisms (e.g., activating and / or controlling one or more actuators). In some examples, the header can be folded and / or unfolded laterally and / or in a forward / backward direction relative to a direction of movement in an agricultural operation. The header deployment correction action can consist of executing a routine to establish a transport state and return the work machine 10 to the harvesting state. The correction action can, for example, consist of stowing the header if it is determined that the header is not in the intended harvesting state. The controller 202 can send one or more signals to actuators and / or systems of the work machine 10, such as...Systems within the harvester header send signals to activate actuators to move the harvester header between an unfolded and a stowed position.

[0105] As described above, in some examples the deployment state may be based, at least in part, on the presence of an obstacle in a disturbance path with the header. In some such examples, the corrective action may include providing a warning regarding the obstacle and / or stopping and / or preventing movement of the header to avoid interaction with the obstacle in a disturbance path with the header. In some examples where the controller 202 can determine a type of obstacle, the controller 202 may issue instructions based on the type of obstacle detected (e.g., type of alarm, speed of actuation / stopping, etc.).

[0106] The phrase "e.g." is used here to indicate non-exhaustive listing of examples and has the same meaning as alternative illustrative phrases such as "including," "including, but not limited to," and "including without limitation." Enumerations with elements separated by conjunctions (e.g., "and") and further preceded by the phrase "one or more of" or "at least one of" indicate, unless otherwise restricted or modified, configurations or arrangements that may include individual elements of the enumeration or any combination thereof. For example, "at least one of A, B, and C" or "one or more of A, B, and C" each indicate the possibilities of only A, only B, only C, or any combination of two or more of A, B, and C (e.g.,A and B; B and C; A and C; or A, B and C).

[0107] To the average person skilled in the art, it is obvious that terms such as "above," "below," "upwards," "downwards," "upper," "lower," etc., are used descriptively for the figures and do not represent any limitations on the scope of protection of the disclosure as defined by the pending claims. Furthermore, the teachings presented here may be described with respect to functional and / or logical block components and / or various processing steps. It is understood that such block components may contain any number of hardware, software, and / or firmware components configured to perform the specified functions.

[0108] Expressions relating to a degree, such as "general", "essentially" or "approximately", refer, according to the understanding of the person skilled in the art, to reasonable ranges outside of a specified value or orientation, e.g. general tolerances or positional relationships associated with the manufacture, assembly and use of the described embodiments.

[0109] Although the foregoing describes examples of the present disclosure, these descriptions are not to be construed as limitations. Rather, other variations and modifications may be made without deviating from the scope of protection and nature of the present disclosure, as defined in the pending claims.

[0110] The preceding description and examples were presented merely to illustrate the disclosure and are not intended to be limiting. Each of the disclosed aspects and examples of the present disclosure may be considered individually or in combination with other aspects, examples, and variations of the disclosure. Furthermore, unless otherwise stated, none of the steps of the procedures of the present disclosure are restricted to a particular order of execution. Variations of the disclosed examples that encompass the basic idea and essence of the disclosure are obvious to a person skilled in the art, and such variations are within the scope of the present disclosure. In addition, all the documents cited herein are fully incorporated by reference.

[0111] Orientation terms used herein, such as "top," "bottom," "horizontal," "vertical," "longitudinal," "lateral," and "end," are used in the context of the example shown. However, the present disclosure should not be limited to the orientation shown. Indeed, other orientations are possible and fall within the scope of this disclosure. Terms used herein for circular shapes, such as diameter or radius, are not to be understood as requiring perfectly circular structures, but should be applied to any suitable structure with a cross-sectional area that can be measured from side to side. Terms that refer generally to shapes, such as...“Circular” or “cylindrical” or “semicircular” or “half-cylindrical” or related or similar terms need not strictly correspond to the mathematical definitions of circles or cylinders or other structures, but may also include structures that represent a reasonable approximation.

[0112] Conditional language used herein, such as "can," "could," "may," "e.g.," and the like, is generally intended, unless explicitly stated otherwise or understood differently in the context, to convey that some examples include certain features, elements, and / or states, while other examples do not. Thus, such conditional language is generally not intended to imply that features, elements, blocks, and / or states are in any way required for one or more examples, or that one or more examples necessarily contain logic to decide, with or without author input or prompting, whether these features, elements, and / or states should be included in or implemented within a particular example.

[0113] Connective language, such as the phrase "at least one of X, Y, and Z," is generally understood, unless explicitly stated otherwise, within the context of use, to convey that an element, label, etc., can be any of X, Y, or Z. Thus, such connective language is generally not intended to imply that certain examples require the presence of at least one of X, at least one of Y, and at least one of Z.

[0114] The expressions "approximately," "about," and "essentially," as used here, represent an amount close to the stated amount that still performs a desired function or achieves a desired result. For example, in some instances, as the context may determine, the expressions "approximately," "about," and "essentially" may refer to an amount that is in the range of 10% or less of the stated amount. The expression "generally," as used here, represents a value, amount, or characteristic that predominantly contains or tends toward a certain value, amount, or characteristic. As an example, in certain instances, as the context may determine, the expression "generally parallel" may refer to something that deviates from exactly parallel by 20 degrees or less.

[0115] Unless explicitly stated otherwise, articles such as "a" or "an" should generally be interpreted as containing one or more described elements. Similarly, phrases such as "a device configured to perform" are intended to include one or more named devices. One or more such named devices may be configured together to perform the specified action. For example, "a processor configured to perform A, B, and C" may include a first processor configured to perform A, working in conjunction with a second processor configured to perform B and C.

[0116] The terms "comprehensive," "including," "with," and the like are synonymous and are used inclusively and openly, not excluding further elements, features, processes, operations, etc. Likewise, the terms "some," "certain," and the like are synonymous and are used openly. Furthermore, the term "or" is used in its inclusive sense (and not its exclusive sense), such that when used, for example, to join a list of elements, the term "or" means one, some, or all of the elements in the list.

[0117] Overall, the language of the claims should be interpreted broadly based on the language used in the claims themselves. The language of the claims should not be limited to the non-exclusive examples illustrated and described in this disclosure or discussed during the pursuit of the application.

[0118] Although systems and methods for detecting and handling working machine conditions have been disclosed in connection with specific examples, this disclosure extends beyond the specifically disclosed examples to other alternative examples and / or uses of the examples, and certain modifications and equivalents thereof. Various features and aspects of the presented examples can be combined or substituted for others to form different types of systems and methods for detecting and handling working machine conditions. The scope of this disclosure should not be limited by the specific disclosed examples described herein.

[0119] Certain features described in this disclosure in connection with separate examples may be implemented in combination in a single example. Conversely, different features described in connection with a single example may be implemented separately in several examples or in any suitable subcombination. Even if features are described herein as acting in certain combinations, one or more features from a claimed combination may, in some cases, be excluded from the combination, and the combination may be claimed as any subcombination or variant of any subcombination.

[0120] While various modifications and alternative forms are possible for the methods and devices described herein, specific examples are shown in the drawings and are described in more detail here. However, it is understood that the invention is not limited to the specific forms or methods disclosed, but rather covers all modifications, equivalents, and alternatives that fall within the scope of the basic ideas and the various examples described and the accompanying claims. Furthermore, the present disclosure of any specific features, aspects, methods, properties, characteristics, qualities, features, elements, or the like, in conjunction with an example, may be used in all further examples presented herein. Methods disclosed herein need not be carried out in the order indicated.Depending on the example, one or more operations, events, or functions of any of the algorithms, procedures, or processes described herein may be performed, added, combined, or omitted entirely in a different sequence (e.g., not all described operations or events are necessary to implement the algorithm). In some examples, operations or events may be performed with temporal overlap, e.g., through multi-threaded processing, interrupt handling, or multiple processors or processor cores, or in other parallel architectures, instead of sequentially. Furthermore, none of the elements, features, blocks, or steps, or groups of elements, features, blocks, or steps, are necessary or indispensable for every example. Additionally, all possible combinations, subcombinations, and rearrangements of systems, procedures, features, elements, modules, blocks, etc., are within the scope of this disclosure.The use of sequential or temporally ordered language, such as "then," "next," "after," "subsequently," and the like, is generally intended to facilitate readability, unless explicitly stated otherwise or understood differently in the context, and is not intended to restrict the sequence of work steps performed. Thus, some examples may be performed using the sequence of work steps described here, while other examples may follow a different sequence.

[0121] Furthermore, even if work steps are shown or described in a specific order in the drawings or the application text, such work steps do not have to be carried out in the specific order shown or in sequential order, and not all work steps need to be carried out to achieve the desired results. Additional work steps not shown or described can be included in the example procedures and processes. For example, one or more additional work steps can be carried out before, after, simultaneously with, or between the work steps described above. Furthermore, the work steps in other examples can be rearranged or reordered.Furthermore, the separation of different system components in the examples described herein should not be understood as requiring such separation in all examples, and it is understood that the described components and systems can generally be integrated together in a single product or encapsulated in several products. Additional examples are also included within the scope of this disclosure.

[0122] Some embodiments have been described in connection with the accompanying figures. Certain figures are drawn and / or shown to scale; however, such a scale is not intended to be limiting, as dimensions and ratios other than those shown are provided and are within the scope of the examples disclosed herein. Distances, angles, etc., are merely illustrative and do not necessarily bear an exact relationship to actual dimensions and the layout of the devices illustrated. Components may be added, removed, and / or rearranged. Furthermore, the present disclosure of any particular features, aspects, methods, properties, characteristics, qualities, features, elements, or the like may be used in conjunction with various examples in all the further examples presented herein.

Claims

[1] Method for detecting crop loss in a machinery operation, the method comprising: Receiving motion speed data from one or more speed detection systems; Receiving crop condition data from one or more feed data sensors; Determine one or more crop condition states at least partially based on one or more of the movement speed data and the crop condition data; Determining crop loss at least partially based on one or more crop condition states; and Adjusting one or more combine harvester operating parameters, at least partially, based on the determination of crop loss. [2] Method according to claim 1, wherein the crop condition data includes crop image data. [3] Method according to claim 1, wherein the crop condition data includes data indicating crop lying down. [4] Method according to claim 1, wherein a crop condition comprises image data indicating crop not supplied. [5] Method according to claim 1, wherein a crop condition comprises a crop condition in front of the working machine during a harvesting operation. [6] Method according to claim 1, wherein a crop condition comprises a crop condition in one or more lateral directions of a working machine during a harvesting operation. [7] Method according to claim 1, wherein one or more combine harvester operating parameters include a header cutting height. [8] Method according to claim 1, wherein one or more combine harvester operating parameters comprise a header downward force. [9] Method according to claim 1, wherein one or more combine harvester operating parameters include an indicator for mechanical changes. [10] Method according to claim 1, wherein one or more combine harvester operating parameters include a header belt speed. [11] Method according to claim 10, wherein adjusting one or more combine harvester operating parameters includes adjusting a belt speed. [12] Method according to claim 1, further comprising identifying one or more rows of crop, wherein the adjustment of one or more combine harvester operating parameters comprises adjusting an orientation of a harvester header in order to align it at least partially with one or more rows of crop. [13] Method according to claim 1, wherein adjusting one or more combine harvester operating parameters comprises reducing a movement speed of the working machine. [14] Method according to claim 1, wherein adjusting one or more combine harvester operating parameters includes stopping the movement of the working machine. [15] System for detecting crop loss in a working machine, the system comprising: one or more controllers configured to: Received movement speed data from one or more speed detection systems; Receive crop condition data from one or more feed data sensors; determine one or more crop condition states at least partially based on one or more of the movement speed data and the crop condition data; Determine crop loss at least partially based on one or more crop condition states; and to adjust one or more combine harvester operating parameters, at least partially, based on the determination of crop loss.

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