Crop State Diagram Generation and Control System

Abstract

One or more infographics are obtained from agricultural machinery. These infographics map the values ​​of one or more agricultural characteristics at different geographical locations in the field. Field sensors on the agricultural machinery sense these agricultural characteristics as the machinery moves through the field. A prediction map generator generates prediction maps of the predicted agricultural characteristics at different locations in the field based on the relationships between the values ​​in the infographics and the agricultural characteristics sensed by the field sensors. These prediction maps can be output and used for automated machine control.

Description

Technical Field

[0001] This manual covers agricultural machinery, forestry machinery, construction machinery, and lawn management machinery. Background Technology

[0002] There are various types of agricultural machinery. Some agricultural machinery includes harvesters, such as combine harvesters, sugarcane harvesters, cotton harvesters, self-propelled forage harvesters, and reapers. Some harvesters can also be equipped with different types of headers to harvest different types of crops.

[0003] Agricultural harvesters can operate in different ways in areas of fields containing lodged crops. Lodged crops refer to crop plants whose stems have been bent or broken due to factors such as wind, rain, or hail. These forces cause the crop stems to bend or break, resulting in a bent and non-vertical orientation of the plant. Crop condition is an agricultural characteristic that indicates whether the crop plant is upright, lodged, partially lodged, with stubble, or missing, as well as the compass orientation and magnitude of lodging in the case of lodged crops.

[0004] The above discussion is provided for general background information only and is not intended to help determine the scope of the subject matter for which protection is sought. Summary of the Invention

[0005] Prior vegetation index maps, seeding characteristic maps, predicted yield maps, or predicted biomass maps are obtained by agricultural harvesters and display one of the vegetation index, seeding characteristic, predicted yield, or predicted biomass values ​​at different geographic locations in the field, and may be particularly useful during the harvesting period. As the agricultural harvester moves through the field, field sensors on the harvester sense characteristics with values ​​indicating the crop status near the harvester. A prediction map generator produces a functional prediction map that predicts the crop status at different locations in the field based on the relationship between the values ​​in the vegetation index, seeding characteristic, predicted yield, or predicted biomass map and the crop status characteristics sensed by the field sensors. The functional predicted crop status map can be output and used for automated machine control.

[0006] This overview is provided to introduce, in a simplified form, some concepts that will be further described in the detailed description below. This overview is not intended to identify key or essential features of the claimed subject matter, nor is it intended to help determine the scope of the claimed subject matter. The claimed subject matter is not limited to examples addressing any or all of the shortcomings mentioned in the background art. Attached Figure Description

[0007] Figure 1 This is a partial schematic diagram of an example of an agricultural harvester.

[0008] Figure 2The following are block diagrams illustrating some parts of an agricultural harvester in more detail, based on some examples of this disclosure.

[0009] Figures 3A to 3B (Referred to herein as Figure 3) shows a flowchart illustrating an example of the operation of an agricultural harvester when generating a diagram.

[0010] Figure 4 This is a block diagram illustrating an example of a prediction model generator and a prediction graph generator.

[0011] Figure 5 This is a flowchart illustrating an example of an agricultural harvester receiving vegetation indices, seeding characteristics, predicted yield or predicted biomass maps, detecting field crop conditions, and generating a predictive crop condition map for use in presenting or controlling the agricultural harvester or both during harvesting operations.

[0012] Figure 6 This is a block diagram illustrating an example of an agricultural harvester communicating with a remote server environment.

[0013] Figures 7 to 9 An example of a mobile device that can be used in agricultural harvesters is shown.

[0014] Figure 10 This is a block diagram illustrating an example of a computing environment that can be used for agricultural harvesters and the architecture shown in the aforementioned figures. Detailed Implementation

[0015] To facilitate an understanding of the principles of this disclosure, reference will now be made to the examples illustrated in the accompanying drawings, and these examples will be described using specific language. However, it should be understood that this disclosure is not intended to limit its scope. Any changes and additional modifications to the described apparatus, systems, and methods, as well as any further application of the principles of this disclosure, are entirely contemplated, as would normally occur to those skilled in the art to which this disclosure pertains. In particular, it is entirely conceivable that features, components, and / or steps described for one example may be combined with features, components, and / or steps described for other examples of this disclosure.

[0016] This specification relates to generating functional prediction maps by combining prior data (previous data) with field data acquired simultaneously with agricultural operations, and more specifically, generating functional prediction crop state maps. In some examples, functional prediction crop state maps can be used to control agricultural machinery, such as combine harvesters. Unless the machine settings also change, the performance of a combine harvester may degrade when it enters areas with different crop states. For example, in areas with lodged crops, the combine harvester may move more slowly through the field to prevent grain loss. Or, for example, in areas with lodged crops, it may be beneficial to guide the combine harvester along a path so that it harvests in the direction opposite to the direction the crop is tilted towards the lodged crop. That is, selecting a path for the combine harvester to drive it into the crop plants from the opposite direction of the lodged plants so that the harvester reaches the top of the plants first. Or, for example, in areas with lodged crops, adjusting the header height or reel position may be beneficial.

[0017] A vegetation index map schematically maps vegetation index values ​​(which can indicate vegetation growth) at different geographic locations within a field of interest. One example of a vegetation index is the normalized difference vegetation index (NDVI). Many other vegetation indices exist and are within the scope of this disclosure. In some examples, vegetation indices can be derived from sensor readings of one or more bands of electromagnetic radiation reflected by the vegetation. Without limitation, these bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

[0018] Vegetation index maps can therefore be used to identify the presence and location of vegetation. In some examples, vegetation index maps enable the identification and georeferencing of crops in the presence of bare soil, crop residues, or other vegetation (including crops or weeds). For example, at the beginning of the growing season, when crops are in their growth stage, vegetation indices can show the progress of crop development. Therefore, if a vegetation index map is generated relatively early in the growing season or midway through the growing season, it can indicate the developmental progress of crop plants. For example, a vegetation index map can indicate whether plants are underdeveloped, whether they are establishing sufficient canopy, or whether other plant attributes indicating plant development are present. Vegetation index maps can also indicate other vegetation characteristics, such as plant health status.

[0019] Seeding characteristic maps illustratively map the location of seeds at different geographic locations in one or more fields of interest. These seed maps are typically collected from past seed planting operations. In some examples, seeding characteristic maps can be derived from control signals used by the seeder during planting or sensor signals generated by sensors on the seeder confirming that seeds have been planted. The seeder may include geographic location sensors that geolocate the location where seeds are planted. This information can be used to determine seed planting density in relation to plant density. For example, plant density may affect crop resistance to windfall. Seeding characteristic maps may also include other information, such as the characteristics of the seeds used. For example, some characteristics include seed type, genetic stem strength, genetic green snap susceptibility, seed brand, seed coat, planting date, germination period, typical growth period, mature plant height, and seed genotype.

[0020] This discussion also includes predictive maps that predict features based on infographics and their relationships with sensed data obtained from field sensors. These predictive maps include predicted yield maps and predicted biomass maps. In one example, a predicted yield map is generated by receiving a prior vegetation index map and sensed yield data obtained from a field yield sensor, determining the relationship between the prior vegetation index map and the sensed yield data obtained from the signal from the field yield sensor, and generating a predicted yield map based on that relationship and the prior vegetation index map using that relationship. In another example, a predicted biomass map is generated by receiving a prior vegetation index map and sensed biomass, determining the relationship between the prior vegetation index map and the sensed biomass obtained from the data signal from a biomass sensor, and generating a predicted biomass map based on that relationship and the prior vegetation index map using that relationship. Predicted yield maps and predicted biomass maps can be created based on other infographics or can be generated in other ways. For example, predicted yield maps or predicted biomass maps can be generated based on satellite imagery, growth models, weather models, etc. Alternatively, for example, a predicted yield map or predicted biomass map may be based in whole or in part on a topographic map, soil type map, soil composition map, or soil health map.

[0021] Therefore, this discussion is directed to an example in which the system receives one or more vegetation indices, seed characteristics, predicted yield maps, or predicted biomass maps during a harvesting operation, and uses field sensors that detect variables indicating crop status. The system generates a model that models the relationship between vegetation index values, seed characteristic values, predicted yield values, or predicted biomass values ​​from one or more received maps and field data from field sensors representing variables indicating crop status. This model is used to generate a functional predicted crop status map that predicts the expected crop status in the field. The functional predicted crop status map generated during the harvesting operation can be presented to an operator or other user for automatic control of an agricultural harvester during the harvesting operation, or both.

[0022] Figure 1 This is a partial schematic diagram of a self-propelled agricultural harvester 100. In the example shown, the agricultural harvester 100 is a combine harvester. Furthermore, although combine harvesters are provided as examples throughout this disclosure, it should be understood that this description is also applicable to other types of harvesters, such as cotton harvesters, sugarcane harvesters, self-propelled hay harvesters, slashers, or other agricultural operating machines. Therefore, this disclosure is intended to cover the various types of harvesters described, and is therefore not limited to combine harvesters. Moreover, this disclosure relates to other types of operating machines, such as agricultural seeders and sprayers in which the generation of predictive maps can be applied, construction equipment, forestry equipment, and lawn management equipment. Therefore, this disclosure is intended to cover these various types of harvesters and other operating machines, and is therefore not limited to combine harvesters.

[0023] like Figure 1 As shown, the agricultural harvester 100 schematically includes an operator's cab 101, which may have various different operator interface mechanisms for controlling the agricultural harvester 100. The agricultural harvester 100 includes front-end equipment, such as a header 102 and a cutter generally indicated by 104. The agricultural harvester 100 also includes a feed chamber 106, a feed accelerator 108, and a thresher generally indicated by 110. The feed chamber 106 and the feed accelerator 108 form part of a material handling subsystem 125. The header 102 is pivotally coupled to the frame 103 of the agricultural harvester 100 along a pivot axis 105. One or more actuators 107 drive the header 102 to move about the axis 105 in a direction generally indicated by arrow 109. Thus, the vertical position (header height) of the header 102 above the ground 111 over which the header 102 travels can be controlled by actuating the actuators 107. Figure 1As not shown, the agricultural harvester 100 may also include one or more actuators that operate to apply a tilt angle, a lateral tilt angle, or both to the header 102 or multiple portions thereof. Tilt refers to the angle at which the cutter 104 engages with the crop. For example, the tilt angle is increased by controlling the header 102 to point the distal edge 113 of the cutter 104 more towards the ground. The tilt angle is decreased by controlling the header 102 to point the distal edge 113 of the cutter 104 more away from the ground. The lateral tilt angle refers to the orientation of the header 102 about the longitudinal axis of the agricultural harvester 100.

[0024] The threshing machine 110 schematically includes a threshing rotor 112 and a set of recesses 114. Additionally, the agricultural harvester 100 includes a separator 116. The agricultural harvester 100 also includes a cleaning subsystem or cleaning device (collectively referred to as cleaning subsystem 118), which includes a cleaning fan 120, a chaffer 122, and a sieve 124. The material handling subsystem 125 also includes a discharge beater 126, a tailings elevator 128, a clean grain elevator 130, and an unloading screw conveyor 134 and a spout 136. The clean grain elevator moves clean grain into a clean grain trough 132. The agricultural harvester 100 also includes a residue subsystem 138, which may include a shredder 140 and a spreader 142. The agricultural harvester 100 also includes a propulsion subsystem comprising an engine that drives ground engagement components 144 (e.g., wheels or tracks). In some examples, combine harvesters within the scope of this disclosure may have more than one of any of the subsystems mentioned above. In some examples, the agricultural harvester 100 may have a left cleaning subsystem and a right cleaning subsystem, a separator, etc., which... Figure 1 Not shown in the image.

[0025] In operation, and as an overview, the combine harvester 100 schematically moves across the field in the direction indicated by arrow 147. As the combine harvester 100 moves, the header 102 (and associated reel 164) engages the crop to be harvested and collects the crop toward the cutter 104. The operator of the combine harvester 100 can be a local human operator, a remote human operator, or an automated system. The operator of the combine harvester 100 can determine one or more of the height setting, tilt angle setting, or side tilt angle setting of the header 102. For example, the operator inputs one or more settings (described in more detail below) to the control system of the control actuator 107. The control system can also receive settings from the operator for establishing the tilt angle and side tilt angle of the header 102 and implement the input settings by controlling associated actuators (not shown) that operate to change the tilt angle and side tilt angle of the header 102. Actuator 107 maintains the cutter head 102 at a height above ground 111 based on a height setting, and, where applicable, at a desired tilt and yaw angle. Each of the height, tilt, and yaw settings can be implemented independently of the others. The control system responds to cutter head errors (e.g., the difference between the height setting and the measured height of the cutter head 102 above ground 111, and, in some examples, tilt and yaw angle errors) with a responsiveness determined based on a selected sensitivity level. If the sensitivity level is set at a higher sensitivity level, the control system responds to smaller cutter head position errors and attempts to reduce the detected errors faster than if the sensitivity level were lower.

[0026] Returning to the description of the operation of the agricultural harvester 100, after the crop is cut by the cutter 104, the cut crop material moves toward the feed accelerator 108 via a conveyor in the feed chamber 106, which accelerates the crop material into the threshing machine 110. The crop material is threshed by the rotor 112, which rotates the crop against the recess 114. The threshed crop material is moved by the separator rotor in the separator 116, where a portion of the residue is moved toward the residue subsystem 138 by the discharge beater 126. This portion of residue conveyed to the residue subsystem 138 is shredded by the residue shredder 140 and spread on the field by the spreader 142. In other configurations, the residue is released from the agricultural harvester 100 into a stockpile. In other examples, the residue subsystem 138 may include a weed seed remover (not shown), such as a seed bagger or other seed collector, or a seed crusher or other seed destroyer.

[0027] The grain falls into the cleaning subsystem 118. A husk sieve 122 separates some of the larger pieces of material from the grain, and a screen 124 separates some of the finer pieces of material from the clean grain. The clean grain falls onto a screw conveyor that moves the grain to the inlet of a clean grain elevator 130, which moves the clean grain upwards, thereby storing it in a clean grain trough 132. Residue is removed from the cleaning subsystem 118 by an airflow generated by a cleaning fan 120. The cleaning fan 120 directs air upwards along an airflow path through the screen and the husk sieve. The airflow carries the residue in the agricultural harvester 100 backwards toward the residue treatment subsystem 138.

[0028] Tail feeder 128 returns the tail feed to thresher 110, where it is re-threshed. Alternatively, the tail feed may be conveyed by tail feeder or another conveyor to a separate re-threshing mechanism, where it is also re-threshed.

[0029] Figure 1 Also shown in one example, the agricultural harvester 100 includes a ground speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a front view image capture mechanism 151 which may be in the form of a stereo camera or a monocular camera, and one or more loss sensors 152 disposed in the cleaning subsystem 118.

[0030] Ground speed sensor 146 senses the travel speed of the agricultural harvester 100 on the ground. Ground speed sensor 146 can sense the travel speed of the agricultural harvester 100 by sensing the rotational speed of ground-engaging components (such as wheels or tracks), drive shafts, axles, or other components. In some cases, a positioning system can be used to sense the travel speed, such as a global positioning system (GPS), dead reckoning system, long range navigation (LORAN) system, or various other systems or sensors that provide an indication of travel speed.

[0031] Loss sensor 152 schematically provides an output signal indicating the amount of grain loss occurring on the right and left sides of cleaning subsystem 118. In some examples, sensor 152 is an impact sensor that counts grain impacts per unit time or per unit distance traveled to provide an indication of grain loss occurring at cleaning subsystem 118. The impact sensors for the right and left sides of cleaning subsystem 118 can provide individual signals or combined or aggregated signals. In some examples, sensor 152 may include a single sensor, rather than providing a separate sensor for each cleaning subsystem 118.

[0032] Separator loss sensor 148 provides indication of the left and right separators (in Figure 1 (Not shown separately) The grain loss signal in the separator. The separator loss sensor 148 can be associated with the left and right separators and can provide separate grain loss signals or combined or aggregated signals. In some cases, various different types of sensors can also be used to sense grain loss in the separator.

[0033] The agricultural harvester 100 may also include other sensors and measuring mechanisms. For example, the agricultural harvester 100 may include one or more of the following sensors: a header height sensor that senses the height of the header 102 above the ground 111; a stability sensor that senses vibration or jumping (and amplitude) of the agricultural harvester 100; a residue setting sensor configured to sense whether the agricultural harvester 100 is configured to chop residue, create a pile, etc.; a cleaning device fan speed sensor for sensing the speed of the cleaning fan 120; a recess gap sensor that senses the gap between the rotor 112 and the recess 114; a threshing rotor speed sensor that senses the rotor speed of the rotor 112; a husk sieve gap sensor that senses the size of the opening in the husk sieve 122; a screen gap sensor that senses the size of the opening in the screen 124; and a material other than grain sensor. A grain (MOG) humidity sensor senses the humidity level of the MOG passing through the harvester 100; one or more machine setting sensors are configured to sense various configurable settings of the harvester 100; a machine orientation sensor senses the orientation of the harvester 100; and a crop property sensor senses various different types of crop properties (such as crop type, crop humidity, and other crop properties). The crop property sensor can also be configured to sense characteristics of the cut crop material as it is processed by the harvester 100. For example, in some cases, the crop property sensor may sense: grain quality (such as broken grain, MOG level); grain composition (such as starch and protein); and the grain feeding rate as the grain travels through the feed chamber 106, the clean grain elevator 130, or other locations within the harvester 100. The crop property sensor may also sense the feeding rate of biomass through the feed chamber 106, the separator 116, or other locations within the harvester 100. The crop property sensor may also sense the feeding rate via the elevator 130 or other parts of the harvester 100 as grain mass flow rate, or provide other output signals indicating other sensed variables. The crop property sensor may include one or more crop state sensors that sense the state of the crop being harvested by the harvester.

[0034] Crop state sensors may include a single-camera or multi-camera system that captures one or more images of crop plants. For example, a forward-view image capture mechanism 151 may form a crop state sensor that senses the crop state of crop plants in front of an agricultural harvester 100. In another example, the crop state sensor may be positioned on the agricultural harvester 100 and viewed in one or more directions other than in front of the agricultural harvester 100. Images captured by the crop state sensor may be analyzed to determine whether the crop is upright, has some degree of lodging, has stubble, or is missing. If the crop has some degree of lodging, the images may then be analyzed to determine the orientation of the lodged crop. Some orientations may be relative to the agricultural harvester 100, such as, but not limited to, “lateral,” “towards the machine,” “away from the machine,” or “random orientation.” Some orientations may be absolute (e.g., relative to the Earth), such as a digital compass heading or a digital deviation from gravity or surface perpendicularity. For example, in some cases, the orientation may be provided relative to magnetic north, relative to true north, relative to the crop row, relative to the harvester heading, or relative to another reference.

[0035] In another example, the crop condition sensor includes a range scanning device, such as, but not limited to, radar, lidar, or sonar. The range scanning device can be used to sense the height of the crop. While crop height indicates other factors, it can also indicate lodged crop, the extent of lodged crop condition, or the orientation of lodged crop.

[0036] Before describing how the agricultural harvester 100 generates a functional predictive crop state map and uses this predictive crop state map for presentation or control, a brief description of some items on the agricultural harvester 100 and their corresponding operations will be provided first. Figure 2The description in Figure 3 illustrates the following: A general type of information map is received, and information from the information map is combined with georeferenced sensor signals generated by field sensors. These sensor signals indicate characteristics of the field, such as the characteristics of crops or weeds present in the field. These field characteristics may include, but are not limited to, field properties such as slope, weed intensity, weed type, soil moisture, and surface quality; crop properties (such as crop height, crop moisture, crop density, and crop condition); grain properties (such as grain moisture, grain size, and grain test weight); and machine performance characteristics (such as loss level, work quality, fuel consumption, and power utilization). The relationship between the characteristic values ​​obtained from the field sensor signals and the information map values ​​is identified, and this relationship is used to generate a new functional prediction map. The functional prediction map predicts values ​​at different geographic locations in the field, and one or more of these values ​​can be used to control one or more subsystems of a machine, such as an agricultural harvester. In some cases, the functional prediction map can be presented to a user, such as an operator of an agricultural operating machine, which may be an agricultural harvester. Function prediction maps can be presented to users in a visual (e.g., via a display), tactile, or auditory manner. Users can interact with function prediction maps to perform editing operations and other user interface actions. In some cases, function prediction maps can be used to control agricultural machinery (such as agricultural harvesters), presented to operators or other users, or presented to operators or users for operator or user interaction, or one or more of these functions.

[0037] In reference Figure 2 After describing the general method in Figure 3, refer to... Figure 4 and Figure 5 A more specific method for generating a functionally predicted crop state map is described, which can be presented to an operator or user, or used to control an agricultural harvester 100, or both. Again, although this discussion is directed to agricultural harvesters, and particularly combine harvesters, the scope of this disclosure extends to other types of agricultural harvesters or other agricultural operating machines.

[0038] Figure 2 This is a block diagram showing some parts of an example agricultural harvester 100. Figure 2The diagram illustrates an agricultural harvester 100, schematically including one or more processors or servers 201, a data storage device 202, a geolocation sensor 204, a communication system 206, and one or more field sensors 208 that sense one or more agricultural characteristics of the field during harvesting operations. Agricultural characteristics can include any characteristic capable of influencing the harvesting operation. Some examples of agricultural characteristics include characteristics of the harvester, the field, the plants on the field, and the weather. Other types of agricultural characteristics are also included. The field sensors 208 generate values ​​corresponding to the sensed characteristics. The agricultural harvester 100 also includes a prediction model or relation generator (collectively referred to as "prediction model generator 210"), a prediction map generator 212, a control area generator 213, a control system 214, one or more controllable subsystems 216, and an operator interface mechanism 218. The agricultural harvester 100 may also include a variety of other agricultural harvester functions 220. Field sensors 208 include, for example, onboard sensors 222, remote sensors 224, and other sensors 226 that sense field characteristics during agricultural operations. Predictive model generator 210 schematically includes an information variable to field variable model generator 228, and may include other items 230. Control system 214 includes a communication system controller 229, an operator interface controller 231, a settings controller 232, a path planning controller 234, a feed rate controller 236, a header and reel controller 238, a belt conveyor controller 240, a platform position controller 242, a residue system controller 244, a machine cleaning controller 245, an area controller 247, and may include other items 246. The controllable subsystem 216 includes a machine and header actuator 248, a propulsion subsystem 250, a steering subsystem 252, a residue subsystem 138, a machine cleaning subsystem 254, and the controllable subsystem 216 may include a variety of other subsystems 256.

[0039] Figure 2The agricultural harvester 100 is also shown to receive one or more information maps 258. As described below, information maps 258 include, for example, vegetation index maps, seeding characteristic maps, predicted yield maps, or predicted biomass maps. However, information maps 258 may also encompass other types of data obtained prior to the harvesting operation, such as contextual information. Contextual information may include, but is not limited to, one or more of the following: weather conditions for part or all of the growing season, presence of pests, geographical location, soil type, irrigation, treatment applications, etc. Weather conditions may include, but are not limited to, precipitation throughout the season, presence of hail that could damage crops, presence of strong winds, wind direction, and temperature throughout the season. Some examples of pests broadly include insects, fungi, weeds, bacteria, viruses, etc. Some examples of treatment applications include herbicides, pesticides, fungicides, fertilizers, mineral supplements, etc. Figure 2 The diagram also illustrates that operator 260 can operate agricultural harvester 100. Operator 260 interacts with operator interface mechanism 218. In some examples, operator interface mechanism 218 may include joysticks, control levers, steering wheels, linkages, pedals, buttons, dials, keypads, user-actuable elements (such as icons, buttons, etc.) on a user interface display device, microphones and speakers (where speech recognition and speech synthesis are provided), and various other types of control devices. Where a touch-sensitive display system is provided, operator 260 may interact with operator interface mechanism 218 using touch gestures. The examples described above are provided as illustrative examples and are not intended to limit the scope of this disclosure. Therefore, other types of operator interface mechanisms 218 may be used, and other types of operator interface mechanisms are within the scope of this disclosure.

[0040] The information map 258 can be downloaded to the agricultural harvester 100 and stored in the data storage device 202 using the communication system 206 or other means. In some examples, the communication system 206 may be a cellular communication system, a system for communication over a wide area network or a local area network, a system for communication over a near-field communication network, or a communication system configured to communicate over any one or a combination of various other networks. The communication system 206 may also include systems that facilitate the downloading or transfer of information to or from a secure digital (SD) card or a universal serial bus (USB) card, or both.

[0041] The geolocation sensor 204 schematically senses or detects the geolocation or orientation of the agricultural harvester 100. The geolocation sensor 204 may include, but is not limited to, a GNSS receiver that receives signals from a Global Navigation Satellite System (GNSS) satellite transmitter. The geolocation sensor 204 may also include a real-time kinematic (RTK) component configured to improve the accuracy of the position data derived from the GNSS signals. The geolocation sensor 204 may include any of a dead reckoning system, a cellular triangulation system, or a variety of other geolocation sensors.

[0042] The field sensor 208 can be referenced above. Figure 1 Any of the sensors described. Field sensor 208 includes an onboard sensor 222 mounted on the agricultural harvester 100. Such a sensor may include, for example, a sensing sensor, an image sensor, or an optical sensor (e.g., a forward-looking monocular or stereo camera system and image processing system), or a camera mounted to view crop plants near the agricultural harvester 100 in addition to viewing the area in front of the harvester 100. Field sensor 208 may also include a remote field sensor 224 for capturing field information. Field data includes data acquired from sensors mounted on the agricultural harvester, or data acquired by any sensor in which data is detected during harvesting operations.

[0043] Predictive model generator 210 generates a model indicating the relationship between values ​​sensed by field sensors 208 and characteristics mapped to the field by infographic 258. For example, if infographic 258 maps vegetation index values ​​to different locations in the field, and field sensors 208 are sensing values ​​indicating crop status, then information variable to field variable model generator 228 generates a predictive crop status model that models the relationship between vegetation index values ​​and crop status values. Predictive map generator 212 uses the predictive crop status model generated by predictive model generator 210 to generate a functional predictive crop status map based on infographic 258, which predicts the crop status values ​​at different locations in the field. Predictive map generator 212 can use the vegetation index values ​​in infographic 258 and the model generated by predictive model generator 210 to generate a functional predictive map 263, which predicts the crop status at different locations in the field. Predictive map generator 212 therefore outputs predictive map 264.

[0044] Alternatively, for example, if InfoMap 258 maps sowing characteristics to different locations in the field, and Field Sensor 208 is sensing values ​​indicating crop status, then Information Variable to Field Variable Model Generator 228 generates a predictive crop status model that models the relationship between sowing characteristics (with or without contextual information) and field crop status values. Prediction Map Generator 212 uses the predictive crop status model generated by Prediction Model Generator 210 to generate a functional predictive crop status map based on InfoMap 258. This functional predictive crop status map predicts the values ​​of crop status that will be sensed by Field Sensor 208 at different locations in the field. Prediction Map Generator 212 can use the sowing characteristic values ​​in InfoMap 258 and the model generated by Prediction Model Generator 210 to generate a functional prediction map 263 that predicts crop status at different locations in the field. Prediction Map Generator 212 then outputs Prediction Map 264.

[0045] In some examples, the data type in Functional Prediction Chart 263 may be the same as the field data type sensed by Field Sensor 208. In some cases, the data type in Functional Prediction Chart 263 may have a different unit than the data sensed by Field Sensor 208. In some examples, the data type in Functional Prediction Chart 263 may be different from the data type sensed by Field Sensor 208, but related to it. For example, in some examples, the field data type may indicate the type of data in Functional Prediction Chart 263. In some examples, the data type in Functional Prediction Chart 263 may be different from the data type in Infographic 258. In some cases, the data type in Functional Prediction Chart 263 may have a different unit than the data in Infographic 258. In some examples, the data type in Functional Prediction Chart 263 may be different from the data type in Infographic 258, but related to it. For example, in some examples, the data type in Infographic 258 may indicate the type of data in Functional Prediction Chart 263. In some examples, the type of data in Functional Prediction Chart 263 is different from one or both of the field data type sensed by Field Sensor 208 and the data type in Infographic 258. In some examples, the type of data in Functional Prediction Chart 263 is the same as one or both of the field data type sensed by Field Sensor 208 and the data type in Infographic 258. In some examples, the type of data in Functional Prediction Chart 263 is the same as one of the field data type sensed by Field Sensor 208 or the data type in Infographic 258, but different from the other.

[0046] like Figure 2As shown, prediction map 264 uses information values ​​from information map 258 at different locations (or locations with similar contextual information) and prediction model 350 to predict the values ​​of characteristics sensed by field sensors 208 at these locations, or characteristics related to characteristics sensed by field sensors 208. For example, if prediction model generator 210 has generated a prediction model indicating the relationship between vegetation index values ​​and crop status, then, given vegetation index values ​​at different locations on the field, prediction map generator 212 generates prediction map 264 predicting the values ​​of crop status at different locations on the field. The vegetation index values ​​at these locations obtained from information map 258 and the relationship between vegetation index values ​​and crop status obtained from prediction model 350 are used to generate prediction map 264.

[0047] The following will describe some changes in the data types mapped in Infographic 258, the data types sensed by Field Sensor 208, and the data types predicted in Prediction Graph 264.

[0048] In some examples, the data type in Infographic 258 differs from the data type sensed by Field Sensor 208, but the data type in Prediction Graph 264 is the same as the data type sensed by Field Sensor 208. For example, Infographic 258 could be a vegetation index map, and the variable sensed by Field Sensor 208 could be a dynamic characteristic. Prediction Graph 264 could then be a predicted crop state map that maps predicted crop state values ​​to different geographic locations in the field. In another example, Infographic 258 could be a vegetation index map, and the variable sensed by Field Sensor 208 could be crop height. Prediction Graph 264 could then be a predicted crop height map that maps predicted crop height values ​​to different geographic locations in the field. Furthermore, in some examples, the data type in Infographic 258 differs from the data type sensed by Field Sensor 208, and the data type in Prediction Graph 264 differs from both the data type in Infographic 258 and the data type sensed by Field Sensor 208. For example, Infographic 258 could be a vegetation index map, and the variable sensed by Field Sensor 208 could be crop height. Predictive graph 264 can then be a predicted biomass map that maps predicted biomass values ​​to different geographic locations in the field. In another example, infographic 258 can be a vegetation index map, and the variable sensed by field sensor 208 can be crop status. Predictive graph 264 can then be a predicted speed map that maps predicted harvester speed values ​​to different geographic locations in the field.

[0049] In some examples, Infographic 258 originates from a field previously traversed during a priori operations and its data type differs from the data type sensed by field sensor 208, but the data type in Predictive Graph 264 is the same as that sensed by field sensor 208. For example, Infographic 258 could be a seed population map generated during planting, and the variable sensed by field sensor 208 could be stem size. Predictive Graph 264 could then be a predicted stem size map that maps predicted stem size values ​​to different geographic locations in the field. In another example, Infographic 258 could be a seed genotype map, and the variable sensed by field sensor 208 could be crop state, such as upright or lodged crop. Predictive Graph 264 could then be a predicted crop state map that maps predicted crop state values ​​to different geographic locations in the field.

[0050] In some examples, Infographic 258 is derived from a field previously traversed during a priori operation, and its data type is the same as that sensed by field sensor 208, and the data type in prediction graph 264 is also the same as that sensed by field sensor 208. For example, Infographic 258 could be a yield map generated during the previous year, and the variable sensed by field sensor 208 could be yield. Prediction graph 264 could then be a predicted yield map that maps predicted yield values ​​to different geographic locations within the field. In such an example, prediction model generator 210 can use relative yield differences from the georeferenced Infographic 258 from the previous year to generate a predictive model that models the relationship between relative yield differences on Infographic 258 and yield values ​​sensed by field sensor 208 during the current harvest operation. The predictive model is then used by prediction graph generator 212 to generate the predicted yield map.

[0051] In another example, Infographic 258 could be a weed intensity map generated during a priori operation, such as from a sprayer, and the variable sensed by field sensor 208 could be weed intensity. Prediction map 264 could then be a predicted weed intensity map that maps predicted weed intensity values ​​to different geographic locations in the field. In such an example, a map of weed intensity at spraying time is georeferenced and provided to agricultural harvester 100 as Infographic 258 of weed intensity. Field sensor 208 can detect weed intensity at geographic locations in the field, and prediction model generator 210 can then build a predictive model that models the relationship between weed intensity at harvest and weed intensity at spraying time. This is because the sprayer affects weed intensity at spraying time, but weeds may reappear in similar areas at harvest time. However, the weed area at harvest time may have different intensities based on harvest time, weather, weed type, etc.

[0052] In some examples, prediction graph 264 may be provided to control region generator 213. Control region generator 213 groups the multiple adjacent portions of a region into one or more control regions based on data values ​​associated with multiple adjacent portions of a region in prediction graph 264. A control region may include two or more consecutive portions of a region (such as a field), for which the control parameters corresponding to the control region used to control the controllable subsystem are constant. For example, the response time of changing the settings of controllable subsystem 216 may not be satisfactory in responding to changes in values ​​contained in a graph such as prediction graph 264. In this case, control region generator 213 analyzes the graph and identifies control regions with defined dimensions to accommodate the response time of controllable subsystem 216. In another example, the size of the control region may be determined to reduce wear caused by excessive actuator movement due to continuous adjustment. In some examples, different groups of control regions may be available for each controllable subsystem 216 or group of controllable subsystems 216. Control regions may be added to prediction graph 264 to obtain prediction control region graph 265. The predicted control area map 265 can therefore be similar to the predicted map 264, except that the predicted control area map 265 includes control area information that defines the control area. Therefore, as described herein, the functional predicted map 263 may or may not include a control area. Both the predicted map 264 and the predicted control area map 265 are functional predicted maps 263. In one example, the functional predicted map 263 does not include a control area, as in the predicted map 264. In another example, the functional predicted map 263 does include a control area, as in the predicted control area map 265. In some examples, if an intercropping production system is implemented, multiple crops may coexist in the field. In this case, the predicted map generator 212 and the control area generator 213 are able to identify the location and characteristics of two or more crops, and then generate the predicted map 264 and the predicted control area map 265 with control areas accordingly.

[0053] It should also be understood that the control region generator 213 can cluster values ​​to generate control regions, and these control regions can be added to the predicted control region map 265 or a separate map, thus showing only the generated control regions. In some examples, the control regions can be used to control or calibrate the agricultural harvester 100 or both. In other examples, the control regions can be presented to the operator 260 for controlling or calibrating the agricultural harvester 100, and in still other examples, the control regions can be presented to the operator 260 or another user or stored for later use.

[0054] Predictive map 264 or predictive control area map 265, or both, is provided to control system 214, which generates control signals based on predictive map 264 or predictive control area map 265, or both. In some examples, communication system controller 229 controls communication system 206 to communicate predictive map 264 or predictive control area map 265, or control signals based on predictive map 264 or predictive control area map 265, to other agricultural harvesters harvesting in the same field. In some examples, communication system controller 229 controls communication system 206 to send predictive map 264, predictive control area map 265, or both, to other remote systems.

[0055] Operator interface controller 231 is operable to generate control signals to control operator interface mechanism 218. Operator interface controller 231 is also operable to present operator 260 with prediction map 264 or prediction control area map 265, or other information derived from or based on prediction map 264, prediction control area map 265, or both. Operator 260 can be a local operator or a remote operator. As an example, controller 231 generates control signals to control the display mechanism to display one or both of prediction map 264 and prediction control area map 265 to operator 260. Controller 231 can generate operator-actuable mechanisms that are shown and can be actuated by the operator to interact with the displayed maps. The operator can edit the maps, for example, by correcting crop state values ​​displayed on the maps based on the operator's observation. Setting controller 232 can generate control signals based on prediction map 264, prediction control area map 265, or both to control various settings on agricultural harvester 100. For example, controller 232 can generate control signals to control the machine and header actuator 248. In response to the generated control signals, the machine and header actuator 248 operate to control, for example, screen and chaff screen settings, thresher clearance, rotor settings, cleaning fan speed settings, header height, header function, reel speed, reel position, belt conveyor function (where the harvester 100 is coupled to the belt conveyor header), corn header function, internal distribution control, and other actuators 248 affecting other functions of the harvester 100. Path planning controller 234 schematically generates control signals to control steering subsystem 252 to turn the harvester 100 according to a desired path. Path planning controller 234 can control the path planning system to generate a route for the harvester 100 and can control propulsion subsystem 250 and steering subsystem 252 to turn the harvester 100 along that route. The feed rate controller 236 can control various subsystems (such as the propulsion subsystem 250 and the machine actuator 248) to control the feed rate based on prediction map 264 or prediction control area map 265, or both. For example, when the harvester 100 approaches an area containing crops with lodging conditions greater than a selected threshold, the feed rate controller 236 can reduce the speed of the harvester 100 to ensure acceptable crop feeding performance and that crop material is collected. The header and reel controller 238 can generate control signals to control the header or reel or other header functions. For example, in an area with lodged crops, adjusting the header height or reel position may be beneficial. The belt conveyor controller 240 can generate control signals based on prediction map 264, prediction control area map 265, or both to control the belt conveyor belt or other belt conveyor functions.The tabletop position controller 242 can generate control signals based on prediction map 264 or prediction control area map 265, or both, to control the position of the tabletop included on the harvester, and the residue system controller 244 can generate control signals based on prediction map 264 or prediction control area map 265, or both, to control the residue subsystem 138. The machine cleaning controller 245 can generate control signals to control the machine cleaning subsystem 254. For example, based on the different types of seeds or weeds passing through the harvester 100, specific types of machine cleaning operations or the frequency of performing cleaning operations can be controlled. Other controllers included on the harvester 100 can also control other subsystems based on prediction map 264 or prediction control area map 265, or both.

[0056] Figure 3A and Figure 3B (Hereinafter referred to as Figure 3) shows a flowchart illustrating an example of the operation of an agricultural harvester 100 in generating a prediction map 264 and a prediction control area map 265 based on information map 258.

[0057] At 280, the agricultural harvester 100 receives Infographic 258. Examples of Infographic 258 or receiving Infographic 258 are discussed with respect to boxes 282, 284, 285, and 286. As discussed above, as shown in box 282, Infographic 258 maps the values ​​of variables corresponding to a first characteristic to different locations in the field. As shown in box 281, receiving Infographic 258 may include selecting one or more of a plurality of possible Infographics available. For example, one Infographic may be a vegetation index map generated from aerial imagery. Another Infographic may be a map generated during a previous pass through the field, which may be performed by a different machine (such as a sprayer or other machine) performing the previous operation in the field. The process of selecting one or more Infographics may be manual, semi-automatic, or automatic. Infographic 258 is based on data collected prior to the current harvesting operation. This is indicated by box 284. For example, the data may be collected based on aerial imagery acquired during the previous year, early in the current growing season, or at other times, or the data may be measurements acquired during the previous year, early in the current growing season, or at other times. The information can also be data detected using other methods (other than aerial imagery). For example, Infographic 258 can be transmitted to agricultural harvester 100 using communication system 206 and stored in data storage device 202.

[0058] Infographic 258 can also be a prediction map, as represented by box 285. As described above, the prediction map can include, for example, a predicted yield map or predicted biomass map generated in part based on a prior vegetation index map or other infographic and field sensor values. In some examples, the predicted yield map or predicted biomass map can be based wholly or partially on a topographic map, soil type map, soil composition map, or soil health map. Predicted yield maps or predicted biomass maps can also be predicted and generated in other ways.

[0059] Infographic 258 can also be otherwise mounted on the agricultural harvester 100 using communication system 206, and this is represented by box 286 in the flowchart of Figure 3. In some examples, Infographic 258 can be received by communication system 206.

[0060] At the start of the harvesting operation, field sensor 208 generates sensor signals indicating one or more field data values ​​that indicate plant characteristics, such as crop status, as shown in box 288. Examples of field sensor 288 are discussed with respect to boxes 222, 290, and 226. As explained above, field sensor 208 includes airborne sensor 222, remote field sensor 224 (such as a UAV-based sensor that collects field data on each flight (as shown in box 290)), or other types of field sensors specified by field sensor 226. In some examples, position, heading, or speed data from geolocation sensor 204 is georeferenced against data from airborne sensors.

[0061] The predictive model generator 210 controls the information variable to field variable model generator 228 to generate a model that models the relationship between the mapped values ​​contained in the information graph 258 and the field values ​​sensed by the field sensor 208, as shown in box 292. The characteristics or data types represented by the mapped values ​​in the information graph 258 and the field values ​​sensed by the field sensor 208 can be the same or different characteristics or data types.

[0062] The relation or model generated by the prediction model generator 210 is provided to the prediction map generator 212. The prediction map generator 212 uses the prediction model and the information map 258 to generate a prediction map 264, which predicts the values ​​of different characteristics sensed by the field sensor 208 at different geographical locations in the field being harvested, or the values ​​of different characteristics related to the characteristics sensed by the field sensor 208, as shown in box 294.

[0063] It should be noted that in some examples, Infographic 258 may include two or more different plots or two or more different layers of a single plot. Each layer may represent a data type different from that of another layer, or the layers may have the same data type acquired at different times. Each plot in the two or more different plots, or each layer in the two or more different layers of a plot, maps different types of variables to geographic locations in the field. In such an example, Predictive Model Generator 210 generates a predictive model that models the relationship between the field data and each of the different variables mapped by the two or more different plots or two or more different layers. Similarly, Field Sensors 208 may include two or more sensors, each sensing different types of variables. Therefore, Predictive Model Generator 210 generates a predictive model that models the relationship between each type of variable mapped by Infographic 258 and each type of variable sensed by Field Sensors 208. The prediction map generator 212 can use each of the graphs or layers in the prediction model and infographic 258 to generate a functional prediction map 263 that predicts the value of each sensed property (or property associated with the sensed property) at different locations in the field being harvested, as sensed by the field sensor 208.

[0064] Prediction map generator 212 configures prediction map 264 such that prediction map 264 can be operated (or consumed) by control system 214. Prediction map generator 212 can provide prediction map 264 to control system 214 or to control region generator 213 or both. Examples of different ways in which prediction map 264 can be configured or output are described with respect to boxes 296, 295, 299, and 297. For example, prediction map generator 212 configures prediction map 264 such that prediction map 264 includes values ​​that can be read by control system 214 and used as the basis for generating control signals for one or more of the different controllable subsystems of agricultural harvester 100, as shown in box 296.

[0065] Control region generator 213 can divide prediction map 264 into control regions based on values ​​on prediction map 264. Values ​​of consecutive geographic locations within each other's thresholds can be grouped into control regions. Thresholds can be default thresholds, or thresholds can be set based on operator input, input from the automation system, or other criteria. The size of the regions can be based on the responsiveness of control system 214, controllable subsystem 216, wear considerations, or other criteria, as shown in box 295. Prediction map generator 212 configures prediction map 264 for presentation to operators or other users. Control region generator 213 can configure prediction control region map 265 for presentation to operators or other users. This is indicated by box 299. When presented to operators or other users, the presentation of prediction map 264 or prediction control region map 265, or both, can include geographic location-related predicted values ​​on prediction map 264, geographic location-related control regions on prediction control region map 265, and one or more setting values ​​or control parameters used based on the predicted values ​​on prediction map 264 or the regions on prediction control region map 265. In another example, the presentation may include more abstract or more detailed information. The presentation may also include a confidence level indicating the accuracy with which predicted values ​​on prediction map 264 or regions on prediction control area map 265 conform to measurements that can be measured by sensors on the harvester 100 as it moves across the field. Additionally, where information is presented to more than one location, an authentication and authorization system may be provided to implement the authentication and authorization process. For example, there may be a hierarchy of individuals authorized to view and modify maps and other presented information. As an example, an onboard display device may display the map locally on the machine in near real-time, or the map may be generated at one or more remote locations, or both. In some examples, each physical display device at each location may be associated with a person or user permission level. User permission levels may be used to determine which display elements are visible on the physical display devices and which values ​​the corresponding person can change. For example, the local operator of harvester 100 may not be able to see the information corresponding to prediction map 264 or make any changes to the machine's operation. However, a supervisor, such as one at a remote location, might be able to see Predictive Map 264 on a monitor but be prevented from making any changes. A manager at a separate, remote location might be able to see all elements on Predictive Map 264 and also be able to modify it. In some cases, Predictive Map 264, accessible and modifiable by a remote manager, can be used for machine control. This is an example of an authorization hierarchy that can be implemented. Predictive Map 264 or Predictive Control Area Map 265, or both, can also be configured in other ways, as shown in box 297.

[0066] At box 298, the control system receives input from the geolocation sensor 204 and other field sensors 208. Specifically, at box 300, the control system 214 detects input from the geolocation sensor 204 identifying the geolocation of the harvester 100. Box 302 indicates that the control system 214 receives sensor input indicating the trajectory or heading of the harvester 100, and box 304 indicates that the control system 214 receives the speed of the harvester 100. Box 306 indicates that the control system 214 receives other information from various field sensors 208.

[0067] At block 308, control system 214 generates a control signal to control controllable subsystem 216 based on prediction map 264 or prediction control area map 265, or both, and inputs from geographic location sensor 204 and any other field sensors 208. At block 310, control system 214 applies the control signal to the controllable subsystem. It should be understood that the specific control signal generated and the specific controllable subsystem 216 controlled can vary based on one or more different factors. For example, the generated control signal and the controllable subsystem 216 controlled can be based on the type of prediction map 264 or prediction control area map 265, or both, being used. Similarly, the timing of the generated control signal, the controllable subsystem 216 controlled, and the control signal can be based on various delays in the crop flow through agricultural harvester 100 and the responsiveness of controllable subsystem 216.

[0068] As an example, the generated prediction map 264, in the form of a predicted crop state map, can be used to control one or more controllable subsystems 216. For example, the functional predicted crop state map may include crop state values ​​at a geographically referenced location within a field being harvested. The functional predicted crop state map can be extracted and used to control, for example, steering and propulsion subsystems 252 and 250. By controlling steering and propulsion subsystems 252 and 250, the feeding rate of material or grain moving through the agricultural harvester 100 can be controlled. Or, for example, by controlling steering and propulsion subsystems 252 and 250, the crop can be kept tilted in the opposite direction to lodged crops. Similarly, the header height can be controlled to collect more or less material (in some cases, the header must be lowered to ensure crop contact), and therefore, the header height can also be controlled to control the feeding rate of material through the agricultural harvester 100. In other examples, if the crop state in front of the machine is predicted as shown in Figure 264, where the crop is lodged along one part of the header but not on another part, or if the crop is lodged more significantly along one part of the header relative to the other part, the header can be controlled to tilt, lateralize, or both to collect the lodged crop more efficiently. The foregoing examples involving the use of functional prediction crop state maps for feed rate and header control are provided only as examples. Therefore, values ​​obtained from prediction crop state maps or other types of functional prediction maps can be used to generate a variety of other control signals to control one or more controllable subsystems 216.

[0069] At box 312, it is determined whether the harvesting operation has been completed. If the harvesting is not completed, the process proceeds to box 314, where field sensor data from geolocation sensor 204 and field sensor 208 (and possibly other sensors) are read.

[0070] In some examples, at box 316, the agricultural harvester 100 can also detect learning trigger criteria to perform machine learning on one or more of the following: the prediction map 264, the prediction control region map 265, the model generated by the prediction model generator 210, the region generated by the control region generator 213, one or more control algorithms implemented by the controller in the control system 214, and other trigger learning criteria.

[0071] The learning trigger criterion can include any of a variety of different criteria. Some examples of detecting the trigger criterion are discussed with regard to boxes 318, 320, 321, 322, and 324. For example, in some examples, triggering learning may include recreating the relationships used to generate the predictive model when a threshold amount of field sensor data is received from field sensor 208. In such an example, an amount of field sensor data received from field sensor 208 exceeding a threshold triggers or prompts predictive model generator 210 to generate a new predictive model used by predictive map generator 212. Thus, as the agricultural harvester 100 continues its harvesting operation, receiving a threshold amount of field sensor data from field sensor 208 triggers the creation of a new relationship represented by the predictive model generated by predictive model generator 210. Furthermore, the new predictive model can be used to regenerate a new predictive map 264, a predictive control area map 265, or both. Box 318 represents detecting a threshold amount of field sensor data used to trigger the creation of a new predictive model.

[0072] In other examples, the learning trigger criterion can be based on the degree of change in field sensor data from field sensor 208, such as the degree of change over time or compared to previous values. For example, if the change within the field sensor data (or the relationship between the field sensor data and the information in Infographic 258) is within a selected range, less than a defined amount, or below a threshold, a new prediction model is not generated by prediction model generator 210. As a result, prediction map generator 212 does not generate a new prediction map 264, prediction control region map 265, or both. However, for example, if the change within the field sensor data is outside the selected range, greater than a defined amount, or above a threshold, prediction model generator 210 uses all or part of the newly received field sensor data used by prediction map generator 212 to generate a new prediction map 264 to generate a new prediction model. At box 320, changes in the field sensor data (such as the magnitude of the amount by which the data exceeds a selected range or the magnitude of the change in the relationship between the field sensor data and the information in Infographic 258) can be used as triggers to cause the generation of new prediction models and prediction maps. Continuing with the example described above, thresholds, ranges, and defined quantities can be set to default values, set by operators or users through user interface interaction, set by the automation system, or otherwise.

[0073] Other learning trigger criteria can also be used. For example, if the predictive model generator 210 switches to a different infographic (different from the initially selected infographic 258), switching to a different infographic can trigger relearning by the predictive model generator 210, the predictive map generator 212, the control area generator 213, the control system 214, or others. In another example, the transition of the agricultural harvester 100 to different terrain or to a different control area can also be used as a learning trigger criterion.

[0074] In some cases, operator 260 can also edit the prediction graph 264 or the prediction control area graph 265, or both. Editing can change the values ​​on the prediction graph 264, or change the size, shape, position, or presence of the control area on the prediction control area graph 265, or both. Box 321 shows that the edited information can be used as a learning trigger criterion.

[0075] In some cases, operator 260 may also observe that the automatic control of the controllable subsystem is not as the operator expects. In this case, operator 260 may provide manual adjustments to the controllable subsystem, reflecting operator 260's expectation that the controllable subsystem will operate in a manner different from that commanded by control system 214. Therefore, manual changes to the settings by operator 260 may result in one or more of the following: based on operator 260's adjustments (as shown in box 322), predictive model generator 210 relearns the model, predictive graph generator 212 regenerates graph 264, control region generator 213 regenerates one or more control regions on predictive control region graph 265, and control system 214 relearns the control algorithm or performs machine learning on one or more of the controller components 232 to 246 in control system 214. Box 324 indicates the use of other triggering learning criteria.

[0076] In other examples, relearning can be performed periodically or intermittently, for example based on a chosen time interval, such as a discrete time interval or a variable time interval, as shown in box 326.

[0077] If relearning is triggered (whether based on a learning trigger criterion or on the elapsed time interval, as shown in box 326), one or more of the predictive model generator 210, predictive map generator 212, control region generator 213, and control system 214 perform machine learning to generate a new predictive model, a new predictive map, a new control region, and a new control algorithm, respectively, based on the learning trigger criterion. The new predictive model, the new predictive map, and the new control algorithm are generated using any additional data collected since the last learning operation. The execution of relearning is indicated by box 328.

[0078] If the harvesting operation is complete, the operation moves from box 312 to box 330, where one or more of the prediction map 264, the prediction control area map 265, and the prediction model generated by the prediction model generator 210 are stored. The prediction map 264, the prediction control area map 265, and the prediction model can be stored locally on the data storage device 202 or sent to a remote system for later use using the communication system 206.

[0079] It will be noted that while some examples in this paper describe the predictive model generator 210 and the predictive graph generator 212 receiving information graphs when generating the predictive model and the functional predictive graph, respectively, in other examples, the predictive model generator 210 and the predictive graph generator 212 may receive other types of graphs, including predictive graphs, such as the functional predictive graph generated during the harvesting operation, when generating the predictive model and the functional predictive graph, respectively.

[0080] Figure 4 yes Figure 1 A block diagram of a portion of an agricultural harvester 100 is shown. Specifically, Figure 4 Examples of the prediction model generator 210 and the prediction graph generator 212 are shown in more detail. Figure 4 The diagram also illustrates the information flow between the different components shown here. As shown, the prediction model generator 210 receives one or more of the following as infographics: a vegetation index map 332, a seeding characteristic map 333, a predicted yield map 335, or a predicted biomass map 337. The vegetation index map 332 includes geographically referenced vegetation index values. The seeding characteristic map 333 includes geographically referenced seed characteristic values. For example, seed characteristics may include the location and quantity of seeds planted. Furthermore, seed characteristics may include seed type, hereditary stem or stalk strength, genetic susceptibility to lodging, seed sheathing, seed genotype, etc.

[0081] The forecast yield map 335 includes geographically referenced forecast yield values. The forecast yield map 335 can be used... Figure 1 and Figure 2 The process described in [the document] generates the infographic, which includes a vegetation index map or a historical yield map, and field sensors, including yield sensors. Predicted yield maps can also be generated in other ways

[335] .

[0082] Predicted biomass map 337 includes geographic reference predicted biomass values. Predicted biomass map 337 can be used... Figure 2 The process described in Figure 3 generates the infographic, which includes a vegetation index map, and the field sensors include rotor-driven pressure or optical sensors that generate sensor signals indicating biomass. The predicted biomass map 337 can also be generated in other ways.

[0083] In addition to receiving one or more of the vegetation index map 332, seeding characteristic map 333, predicted yield map 335, or predicted biomass map 337 as infographics, the prediction model generator 210 also receives geographic location 334, or an indication of geographic location, from the geographic location sensor 204. The field sensor 208 schematically includes an airborne crop status sensor 336 and a processing system 338. The processing system 338 processes the sensor data from the airborne crop status sensor 336.

[0084] In some examples, the onboard crop condition sensor 336 may be an optical sensor on the harvester 100. The optical sensor may be positioned in front of the harvester 100 to collect images of the field in front of the harvester 100 during harvesting operations as the harvester 100 moves through the field. The processing system 338 processes one or more images acquired via the onboard crop condition sensor 336 to generate processed image data that identifies one or more characteristics of the crop plants in the images. For example, the magnitude and orientation of the crop plants in a lodging state. The processing system 338 may also geolocate values ​​received from the field sensor 208. For example, the location of the harvester 100 when it receives a signal from the field sensor 208 is typically not the exact location of the sensed crop condition. This is because there is a time required from the transmission of the sensor to the harvester 100 (equipped with a geolocation sensor) contacting the crop plants whose crop condition is sensed. In some examples, to take into account transmission sensing, the camera field of view can be calibrated so that the area of ​​the fallen crop in the image captured by the camera can be geolocated based on the location of the area of ​​the fallen crop in the image.

[0085] Other crop status sensors may also be used. In some examples, raw or processed data from the onboard crop status sensor 336 can be presented to the operator 260 via the operator interface mechanism 218. The operator 260 may be onboard the agricultural harvester 100 or located remotely.

[0086] This discussion continues with an example in which the airborne crop state sensor 336 includes an optical sensor (e.g., a camera). It should be understood that this is only one example, and other examples of the sensors mentioned above as airborne crop state sensors 336 are also considered herein. Figure 4 As shown, the prediction model generator 210 includes a vegetation index to crop state model generator 342, a yield to crop state model generator 344, and a biomass to crop state model generator 346. In other examples, the prediction model generator 210 may include... Figure 4The components shown in the examples are additional, fewer, or different from the components. Therefore, in some examples, the predictive model generator 210 may also include other items 348, which may include other types of predictive model generators to generate other types of crop state models.

[0087] Model generator 342 determines the relationship between the on-site crop state data 340 at a geographic location corresponding to the geographic location of the on-site crop state data 340, and the vegetation index values ​​from vegetation index map 332 corresponding to the geographic locations of the crop state data 340 in the field. Based on this relationship established by model generator 342, model generator 342 generates a predictive crop state model. The crop state model is used to predict the crop state at the same location in the field based on the geographic reference vegetation index values ​​contained in vegetation index map 332 at different locations in the field. In some examples, model generator 342 can use the time series of vegetation index map to identify the rate of crop senescence after green snap, increased crop stress from stem damage, etc.

[0088] Model generator 344 determines the relationship between crop status, as represented in field crop status data 340, at a geographic location corresponding to the geolocation of field crop status data 340, and sowing characteristics at the same location. Sowing characteristic values ​​are georeferenced values ​​included in sowing characteristic map 333. Model generator 344 generates a predictive crop status model based on the sowing characteristic values ​​to predict the crop status at a location in the field. The crop status model is used to predict the crop status at the same location in the field based on georeferenced sowing characteristic values ​​included in sowing characteristic map 333 at different locations in the field. For example, a sowing characteristic could be seed planting density.

[0089] Model generator 345 determines the relationship between the crop state, represented in field crop state data 340, at a geographic location corresponding to the geolocation of field crop state data 340, and the predicted yield at the same location. The predicted yield value is a georeferenced value included in the predicted yield map 335. Model generator 345 generates a predicted crop state model based on the predicted yield value to predict the crop state at a location in the field. The crop state model is used to predict the crop state at the same location in the field based on the georeferenced predicted yield values ​​included at different locations in the field in the predicted yield map 335.

[0090] Model generator 346 determines the relationship between crop status, as represented in field crop status data 340, at a geographic location corresponding to the geolocation of field crop status data 340, and predicted biomass at the same location. Predicted biomass values ​​are georeferenced values ​​included in predicted biomass map 337. Model generator 346 generates a predicted crop status model based on the predicted biomass values ​​to predict crop status at locations within the field. The crop status model is used to predict crop status at the same location in the field based on georeferenced predicted biomass values ​​included in predicted biomass map 337 at different locations within the field.

[0091] In view of the above, the prediction model generator 210 is operable to generate multiple prediction crop state models, such as one or more prediction crop state models generated by model generators 342, 344, 345, 346, and 348. In another example, two or more of the above-described prediction crop state models can be combined into a single prediction crop state model, which predicts crop state based on vegetation index values, seeding characteristic values, predicted yield values, or predicted biomass values ​​at different locations in the field. Any one or a combination of these crop state models is generated by... Figure 4 The crop state model 350 in the model is used to represent the crop state.

[0092] The crop state prediction model 350 is provided to the prediction map generator 212. Figure 4 In one example, the prediction map generator 212 includes a crop state map generator 352. In other examples, the prediction map generator 212 may include additional, fewer, or different map generators. The crop state map generator 352 receives a prediction crop state model 350, which predicts crop state based on the relationship between the sensed crop state value and one or more values ​​from a vegetation index map 332, a seeding characteristic map 333, a predicted yield map 335, and a predicted biomass map 337 at the corresponding location where the crop state is sensed.

[0093] The crop state map generator 354 can also generate a functional predictive crop state map 360 predicting the crop state at said different locations in the field based on vegetation index values, sowing characteristic values, predicted yield values, or predicted biomass values ​​at different locations in the field and the predictive crop state model 350. The generated functional predictive crop state map 360 can be provided to the control region generator 213, the control system 214, or both. The control region generator 213 generates control regions and incorporates these control regions into the functional predictive map, i.e., the predictive map 360, to generate a predictive control region map 265. One or both of the functional predictive map 264 or the predictive control region map 265 can be presented to the operator 260 or other users or provided to the control system 214, which generates control signals based on the predictive map 264, the predictive control region map 265, or both to control one or more of the controllable subsystems 216.

[0094] Figure 5 This is a flowchart illustrating an example of the operations of the prediction model generator 210 and the prediction map generator 212 in generating the prediction crop state model 350 and the functional prediction crop state map 360. At box 362, the prediction model generator 210 and the prediction map generator 212 receive one or more of the prior vegetation index map 332, the seeding characteristic map 333, the predicted yield map 335, and the predicted biomass map 337.

[0095] At box 363, the infographic selector 209 selects one or more specific infographics 250 for use by the predictive model generator 210. In some examples, the infographic selector 209 may change the infographic being used when it detects that one of the other infographics is more closely related to the crop state sensed in the field. For example, a change from the vegetation index infographic 332 to the seeding characteristic infographic 333 may occur when the seeding characteristic infographic 333 is better related to the crop state sensed by the field sensors.

[0096] At frame 364, a crop status sensor signal is received from an onboard crop status sensor 336. As described above, the onboard crop status sensor 336 may be an optical sensor 365 or some other crop status sensor 370.

[0097] At box 372, the processing system 338 processes one or more received field sensor signals received from the onboard crop status sensor 336 to generate crop status values ​​that indicate the crop status characteristics of crop plants in the field adjacent to the agricultural harvester 100.

[0098] At box 382, ​​the prediction model generator 210 also obtains the geographic location corresponding to the sensor signal. For example, the prediction model generator 210 can obtain the geographic location from the geographic location sensor 204 and determine the precise geographic location to which the field-sensed crop state belongs based on machine latency (e.g., machine processing speed), machine speed, and sensor considerations (e.g., camera field of view, sensor calibration, etc.). For example, the exact time at which the crop state sensor signal is captured does not typically correspond to the crop state at the current geographic location of the harvester 100. Instead, the current field crop state sensor signal corresponds to the location in the field in front of the harvester 100 because the current field crop state sensor signal is sensed in an image taken in front of the harvester 100. This is indicated by box 378.

[0099] At box 384, prediction model generator 210 generates one or more prediction crop state models, such as crop state model 350, which model the relationship between at least one of vegetation index values, sowing characteristics, predicted yield values, or predicted biomass values ​​obtained from an infographic such as infographic 258, and crop state sensed by field sensor 208. For example, prediction model generator 210 may generate prediction crop state models based on sowing density values ​​and sensed crop state indicated by sensor signals obtained from field sensor 208, wherein the sowing density values ​​may also indicate a tall crop plant population.

[0100] At box 386, a predicted crop state model (e.g., predicted crop state model 350) is provided to a prediction map generator 212, which generates functional predicted crop state maps that map the predicted crop state to different geographic locations in the field based on a prior vegetation index map 332, a sowing characteristic map 333, a predicted yield map 335 or a predicted biomass map 337, and the predicted crop state model 350. For example, in some examples, the functional predicted crop state map 360 predicts crop state. In other examples, the functional predicted crop state map 360 predicts other items. Furthermore, the functional predicted crop state map 360 can be generated during the agricultural harvesting operation. Thus, the functional predicted crop state map 360 is generated when an agricultural harvester passes through a field where an agricultural harvesting operation is being performed.

[0101] At block 394, the prediction map generator 212 outputs a functional predicted crop state map 360. At block 393, the prediction map generator 212 configures the functional predicted crop state map 360 for use by the control system 214. At block 395, the prediction map generator 212 can also provide map 360 to the control region generator 213 to generate a control region. At block 397, the prediction map generator 212 also configures map 360 in other ways. The functional predicted crop state map 360 (with or without a control region) is provided to the control system 214. At block 396, the control system 214 generates control signals based on the functional predicted crop state map 360 to control the controllable subsystem 216.

[0102] Therefore, it can be seen that this system employs an infographic, which maps characteristics such as vegetation indices, seeding characteristics, predicted yields, or predicted biomass values ​​to different locations in the field. The system also uses one or more field sensors to sense characteristics such as crop status and generates a model that models the relationship between the crop status sensed in the field using the field sensors and the characteristics mapped in the infographic. Thus, the system uses the model and the infographic to generate a functional prediction map, and the generated functional prediction map can be configured for use by the control system or presented to local or remote operators or other users. For example, the control system can use the map to control one or more systems of a combine harvester.

[0103] The current discussion has already mentioned processors and servers. In some examples, processors and servers include computer processors with associated memory and timing circuitry (not shown separately). Processors and servers are functional parts of the system or device to which they belong, and are activated and facilitated by other components or items in these systems.

[0104] Furthermore, numerous user interface displays have been discussed. Displays can take various forms and can have various user-actuable operator interface structures set upon them. For example, user-actuable operator interface mechanisms can be text boxes, checkboxes, icons, links, drop-down menus, search boxes, etc. User-actuable operator interface mechanisms can also be actuated in various ways. For example, user-actuable operator interface mechanisms can be actuated using operator interface mechanisms such as click devices (e.g., trackballs or mice, hardware buttons, switches, joysticks or keyboards, thumb switches or thumb pads, etc.), virtual keyboards, or other virtual actuators). Furthermore, if the screen displaying the user-actuable operator interface mechanism is a touch-sensitive screen, touch gestures can be used to actuate the user-actuable operator interface mechanism. Moreover, user-actuable operator interface mechanisms can be actuated using voice commands utilizing speech recognition capabilities. Speech recognition can be implemented using speech detection devices (such as microphones) and software for recognizing the detected speech and executing commands based on the received speech.

[0105] Many data storage devices are also discussed. It should be noted that data storage can be divided into multiple data storage devices. In some examples, one or more data storage devices may be local to the system accessing the data storage devices; one or more data storage devices may all be located remotely from the system utilizing the data storage devices; or one or more data storage devices may be local while others are remote. This disclosure considers all of these configurations.

[0106] Furthermore, the accompanying diagram shows multiple boxes, with each function belonging to a specific box. It should be noted that fewer boxes can be used to illustrate that functions attributed to multiple different boxes are performed by fewer components. Moreover, more boxes can be used to show that the function can be distributed across more components. In different examples, some functions can be added, and some functions can be removed.

[0107] It should be noted that the foregoing discussion has described various different systems, components, logic, and interactions. It should be understood that any one or all of such systems, components, logic, and interactions can be implemented by hardware items, such as processors, memory, or other processing units, performing functions associated with those systems, components, logic, or interactions, as described below. Furthermore, any one or all of the systems, components, logic, and interactions can be implemented by software loaded into memory and subsequently executed by a processor, server, or other computing unit, as described below. Any one or all of the systems, components, logic, and interactions can also be implemented by different combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that can be used to implement any one or all of the systems, components, logic, and interactions described above. Other structures may also be used.

[0108] Figure 6 This is a block diagram of the agricultural harvester 600, which can be similar to... Figure 2 The agricultural harvester 100 is shown in the diagram. The agricultural harvester 600 communicates with components in a remote server architecture 500. In some examples, the remote server architecture 500 can provide computing, software, data access, and storage services without requiring end users to know the physical location or configuration of the system delivering the services. In various examples, the remote server can deliver services over a wide area network (such as the Internet) using appropriate protocols. For example, the remote server can deliver applications over a wide area network and can be accessed via a web browser or any other computing component. Figure 2 The software or components shown herein, along with their associated data, can be stored on servers at remote locations. Computing resources in a remote server environment can be consolidated at a remote data center location, or they can be distributed across multiple remote data centers. Remote server infrastructure can deliver services through a shared data center, even if the service appears as a single access point to the user. Therefore, the components and functions described herein can be provided from remote servers at remote locations using a remote server architecture. Alternatively, components and functions can be provided from servers, or they can be installed directly or otherwise on client devices.

[0109] exist Figure 6 In the examples shown, some items are similar to Figure 2 The items shown are numbered similarly. Figure 6 Specifically, it is shown that the prediction model generator 210 or the prediction graph generator 212, or both, can be located at a server location 502, away from the agricultural harvester 600. Therefore, in Figure 6 In the example shown, the agricultural harvester 600 accesses the system via a remote server location 502.

[0110] Figure 6 Another example of a remote server architecture is also described. Figure 6 It shows Figure 2 Some components can be located at remote server location 502, while others can be located elsewhere. As an example, data storage device 202 can be placed at a location separate from location 502 and accessed via a remote server at location 502. Regardless of their location, these components can be directly accessed by the combine harvester 600 via a network (such as a wide area network or local area network); these components can be hosted on a remote site, provided as a service, or accessed by a connection service residing at a remote location. Furthermore, data can be stored anywhere, and the stored data can be accessed or forwarded to or by an operator, user, or system. For example, a physical carrier can be used in addition to a physical carrier. In some examples, where wireless telecommunications service coverage is poor or absent, another machine (such as a fuel truck or other mobile machine or vehicle) can have an automatic, semi-automatic, or manual information collection system. When the combine harvester 600 approaches the machine (such as a fuel truck) containing the information collection system before refueling, the information collection system collects information from the combine harvester 600 using any type of temporary dedicated wireless connection. Then, when the machine containing the received information reaches a location where wireless telecommunications service coverage or other wireless coverage is available, the collected information can be forwarded to another network. For example, when a fuel truck travels to a location to refuel other machines or at a main fuel storage location, the fuel truck can enter an area with wireless communication coverage. All these architectures are considered in this paper. Furthermore, information can be stored on the agricultural harvester 600 until the agricultural harvester 600 enters an area with wireless communication coverage. The agricultural harvester 600 itself can transmit the information to another network.

[0111] It will also be noted that Figure 2 The components or parts thereof can be located on a variety of different devices. One or more of these devices may include an airborne computer, electronic control unit, display unit, server, desktop computer, laptop computer, tablet computer, or other mobile devices such as PDAs, cellular phones, smartphones, multimedia players, personal digital assistants, etc.

[0112] In some examples, the remote server architecture 500 may include network security measures. Without limitation, these measures include encryption of data on storage devices, encryption of data sent between network nodes, authentication of personnel or processes accessing data, and the use of a ledger for recording metadata, data, data transfer, data access, and data transformation. In some examples, the ledger may be distributed and immutable (e.g., implemented as a blockchain).

[0113] Figure 7 This is a simplified block diagram illustrating a schematic example of a handheld or mobile computing device 16 that can be used as a part of this system (or a user's or customer's handheld device) to be deployed therein. For example, a mobile device could be deployed in the operator's cab of an agricultural harvester 100 for use in generating, processing, or displaying the diagrams discussed above. Figures 8 to 9 Examples are handheld or mobile devices.

[0114] Figure 7 A general block diagram of the components of client device 16, which can operate... Figure 2 The device 16 includes some of the components shown, interacts with them, or both. In device 16, a communication link 13 is provided that allows the handheld device to communicate with other computing devices, and in some examples, the communication link 13 provides a channel for automatically receiving information (e.g., by scanning). Examples of the communication link 13 include those that allow communication via one or more communication protocols, such as wireless services for providing cellular access to a network, and protocols for providing local wireless connectivity to a network.

[0115] In other examples, the application can receive data on a removable Secure Digital (SD) card connected to interface 15. Interface 15 and communication link 13 communicate along bus 19 with processor 17 (which may also be implemented from a processor or server in other figures), which is also connected to memory 21 and input / output (I / O) components 23, as well as clock 25 and position system 27.

[0116] In one example, I / O components 23 are provided to facilitate input and output operations. Various examples of I / O components 23 in device 16 may include input components (such as buttons, touch sensors, optical sensors, microphones, touchscreens, proximity sensors, accelerometers, orientation sensors) and output components (such as display devices, speaker and / or printer ports). Other I / O components 23 may also be used.

[0117] Clock 25 schematically includes a real-time clock component that outputs the time and date. Schematically, it may also provide timing functions for processor 17.

[0118] Location system 27 schematically includes components that output the current geographic location of device 16. This may include, for example, a Global Positioning System (GPS) receiver, a LoRAN system, a dead reckoning system, a cellular triangulation system, or other positioning systems. Location system 27 may also include, for example, mapping or navigation software that generates desired maps, navigation routes, and other geographic functions.

[0119] Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data storage device 37, communication driver 39, and communication configuration settings 41. Memory 21 may include all types of tangible, volatile, and non-volatile computer-readable storage devices. Memory 21 may also include computer storage media (described below). Memory 21 stores computer-readable instructions that, when executed by processor 17, cause the processor to perform computer-implemented steps or functions. Processor 17 may also be activated by other components to facilitate their functionality.

[0120] Figure 8 The illustration shows an example where device 16 is a tablet computer 600. Figure 8 In the diagram, computer 600 is shown as having a user interface display screen 602. Screen 602 can be a touchscreen or a pen-enabled interface that receives input from a pen or stylus. Tablet computer 600 can also utilize an on-screen virtual keyboard. Of course, computer 600 can also be attached to a keyboard or other user input device, for example, via a suitable attachment structure (such as a wireless link or USB port). Computer 600 can also schematically receive voice input.

[0121] Figure 9 Similar to Figure 8 In addition to being a smartphone 71, the smartphone 71 has a touch-sensitive display 73 that shows icons, blocks, or other user input mechanisms 75. Users can use these mechanisms 75 to run applications, make calls, perform data transfer operations, etc. Generally, smartphones 71 are built on a mobile operating system and offer more advanced computing power and connectivity than feature phones.

[0122] Note that other forms of device 16 are possible.

[0123] Figure 10 It is one of the deployable ones Figure 2 An example of a computing environment for the components. Reference Figure 10 An example system for implementing some embodiments includes a computing device in the form of a computer 810 programmed to operate as discussed above. Components of the computer 810 may include, but are not limited to, a processing unit 820 (which may include a processor or server from the previous figures), system memory 830, and a system bus 821 that couples various system components, including the system memory, to the processing unit 820. The system bus 821 may be any of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of various bus architectures. Regarding... Figure 2 The described memory and program can be deployed in Figure 10 In the corresponding part.

[0124] Computer 810 typically includes a variety of computer-readable media. Computer-readable media can be any available medium accessible to computer 810, and includes volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, computer-readable media can include computer storage media and communication media. Computer storage media are distinct from and do not include modulated data signals or carriers. Computer-readable media include hardware storage media, including volatile and non-volatile, removable and non-removable media implemented in any way or by any technique for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disc storage devices, magnetic tape, magnetic tape, disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to computer 810. Communication media can implement computer-readable instructions, data structures, program modules, or other data in a transmission mechanism, and includes any information delivery medium. The term "modulated data signal" refers to a signal that has one or more of its characteristics set or changed in a manner that encodes information in the signal.

[0125] System memory 830 includes computer storage media in the form of volatile and / or non-volatile memory, or both, such as read-only memory (ROM) 831 and random access memory (RAM) 832. The basic input / output system 833 (BIOS) (which contains basic routines such as those that help transfer information between components within computer 810 during startup) is typically stored in ROM 831. RAM 832 typically contains data and / or program modules, or both, that are readily accessible to and / or currently being operated by processing unit 820. This is by way of example and not limitation. Figure 10 The operating system 834, application program 835, other program modules 836, and program data 837 are shown.

[0126] Computer 810 may also include other removable / non-removable volatile / non-volatile computer storage media. This is just one example. Figure 10A hard disk drive 841 is shown that reads from or writes to a non-removable, non-volatile magnetic medium, an optical disk drive 855, and a non-volatile optical disk 856. The hard disk drive 841 is typically connected to the system bus 821 via a non-removable memory interface (such as interface 840), and the optical disk drive 855 is typically connected to the system bus 821 via a removable memory interface (such as interface 850).

[0127] Alternatively or additionally, the functions described herein may be performed at least in part by one or more hardware logic components. For example, but not limited to, illustrative types of hardware logic components that may be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), application-specific standard products (e.g., ASSPs), System-on-a-chip Systems (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.

[0128] The above discussion and Figure 10 The driver and its associated computer storage medium shown provide the computer 810 with storage for computer-readable instructions, data structures, program modules, and other data. For example, in Figure 10 In the diagram, hard disk drive 841 is shown storing operating system 844, application program 845, other program modules 846, and program data 847. Note that these components may be the same as or different from operating system 834, application program 835, other program modules 836, and program data 837.

[0129] Users can input commands and information to computer 810 through input devices such as keyboard 862, microphone 863, and pointing devices 861 (such as mouse, trackball, or touchpad). Other input devices (not shown) may include joysticks, game keyboards, satellite dishes, scanners, etc. These and other input devices are typically connected to processing unit 820 via user input interface 860 coupled to the system bus, but may also be connected via other interfaces and bus structures. Visual display 891 or other types of display devices are also connected to system bus 821 via an interface such as video interface 890. In addition to the monitor, the computer may also include other peripheral output devices, such as speakers 897 and printers 896, which can be connected via peripheral output interface 895.

[0130] Computer 810 operates in a networked environment using a logical connection (such as a controller local area network (CAN), local area network (LAN), or wide area network (WAN)) between one or more remote computers (such as remote computer 880).

[0131] When used in a LAN networking environment, computer 810 connects to LAN 871 via a network interface or adapter 870. When used in a WAN networking environment, computer 810 typically includes a modem 872 or other devices for establishing communication over a WAN 873 (such as the Internet). In a networking environment, program modules can be stored in remote memory storage devices. For example, Figure 10 This demonstrates that remote application 885 can reside on remote computer 880.

[0132] It should also be noted that the different examples described in this article can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of this is considered in this article.

[0133] Example 1 is an agricultural operating machine, comprising:

[0134] A communication system that receives an information map, the information map including values ​​of a first agricultural characteristic corresponding to different geographical locations in the field;

[0135] A geolocation sensor that detects the geolocation of agricultural machinery;

[0136] A field sensor that detects crop status as a second agricultural characteristic corresponding to geographical location;

[0137] A predictive model generator generates a predictive agricultural model based on the value of a first agricultural characteristic at the geographic location in an information map and the value of a second agricultural characteristic sensed by field sensors at the geographic location. The predictive agricultural model models the relationship between the first and second agricultural characteristics.

[0138] A prediction map generator generates a functional predictive agriculture map of a field based on the value of a first agricultural characteristic in an information map and a predictive agriculture model. The functional predictive agriculture map maps the predicted value of the second agricultural characteristic to different geographical locations in the field.

[0139] Example 2 is any or all of the agricultural operating machines of the foregoing examples, wherein a prediction map generator configures the functional predictive agricultural map for use by a control system, which generates control signals based on the functional predictive agricultural map to control controllable subsystems on the agricultural operating machine.

[0140] Example 3 is any or all of the agricultural machinery described in the foregoing examples, wherein the field sensors generate sensor data indicating crop status, and wherein the field sensors include:

[0141] A processing system configured to analyze the sensor data and determine at least one of the orientation or magnitude of the crop state.

[0142] Example 4 is any or all of the agricultural machinery described in the foregoing examples, wherein the infographic includes a priori vegetation index map that maps vegetation index values, which are the first agricultural characteristic, to different geographic locations in the field.

[0143] Example 5 is any or all of the agricultural operation machines of the foregoing examples, wherein the predictive model generator is configured to determine a relationship between a crop state detected at the geographic location and a vegetation index value at the geographic location from the vegetation index values ​​in the prior vegetation index map, and the predictive agricultural model is configured to receive an input vegetation index value as a model input and generate a predicted crop state value as a model output based on the determined relationship.

[0144] Example 6 is any or all of the agricultural operation machines of the foregoing examples, wherein the infographic includes a sowing characteristic map that maps sowing characteristic values ​​of the first agricultural characteristic to different geographical locations in the field.

[0145] Example 7 is any or all of the agricultural operation machines of the foregoing examples, wherein the predictive model generator is configured to determine the relationship between crop state detected at the geographic location and sowing characteristics in the sowing characteristic map at the geographic location, and the predictive agricultural model is configured to receive input sowing characteristic values ​​as model input and generate predictive crop state values ​​as model output based on the determined relationship.

[0146] Example 8 is any or all of the agricultural machinery described in the foregoing examples, wherein the sowing characteristic includes hereditary stem or stalk strength.

[0147] Example 9 is any or all of the agricultural machinery described in the foregoing examples, wherein the sowing characteristics include genetic susceptibility to lodging.

[0148] Example 10 is an agricultural operation machine of any or all of the foregoing examples, wherein the infographic includes a prediction map that maps a predicted yield or predicted biomass value as a first agricultural characteristic to different geographic locations in the field.

[0149] Example 11 is an agricultural operation machine of any or all of the foregoing examples, wherein a prediction model generator is configured to determine a relationship between a crop state detected at the geographic location and a predicted yield or predicted biomass at the geographic location in the prediction map, and the prediction agricultural model is configured to receive an input yield value or an input biomass value as model input and generate a predicted crop state value as model output based on the determined relationship.

[0150] Example 12 is a computer-implemented method for generating functional predictive agricultural maps, the computer-implemented method comprising:

[0151] Receive an information map at the agricultural machinery, the information map indicating the value of a first agricultural characteristic corresponding to different geographical locations in the field;

[0152] Detect the geographical location of the agricultural machinery;

[0153] Crop status is detected using on-site sensors as a second agricultural characteristic corresponding to geographical location;

[0154] Generate a predictive agricultural model that models the relationship between the first agricultural characteristic and the second agricultural characteristic; and

[0155] A predictive map generator is controlled to generate a functional predictive agricultural map of the field based on the value of a first agricultural characteristic in the information map and a predictive agricultural model. The functional predictive agricultural map maps the predicted value of the second agricultural characteristic to different locations in the field.

[0156] Example 13 is a computer-implemented method of any or all of the foregoing examples, and also includes:

[0157] A functional predictive agriculture map is configured for the control system, which generates control signals based on the functional predictive agriculture map to control controllable subsystems on agricultural machinery.

[0158] Example 14 is a computer-implemented method of any or all of the foregoing examples, wherein receiving the information map includes receiving a prior vegetation index map that maps vegetation index values ​​as a first agricultural characteristic to different geographic locations in the field.

[0159] Example 15 is a computer-implemented method of any or all of the foregoing examples, wherein generating a predictive agricultural model includes:

[0160] The relationship between the vegetation index value and the crop status is determined based on the crop status detected at the geographic location and a vegetation index value at the geographic location from the vegetation index values ​​in the prior vegetation index map.

[0161] The control predictive model generator generates a predictive agricultural model based on the determined relationship. The predictive agricultural model receives input vegetation index values ​​as model input and generates predicted crop state values ​​as model output.

[0162] Example 16 is a computer-implemented method of any or all of the foregoing examples, wherein receiving the information map includes receiving a seeding characteristic map that maps seeding characteristic values, which are first agricultural characteristics, to different geographical locations in the field.

[0163] Example 17 is a computer-implemented method of any or all of the foregoing examples, wherein generating a predictive agricultural model includes:

[0164] Based on the crop status and sowing characteristic values ​​at the geographical location detected in the crop status and sowing characteristic map, the relationship between the sowing characteristic values ​​and the crop status is determined; and

[0165] A predictive model generator is controlled to generate a predictive agricultural model based on a determined relationship. The predictive agricultural model receives input sowing characteristic values ​​as model input and generates predicted crop state values ​​as model output.

[0166] Example 18 is a computer-implemented method of any or all of the foregoing examples, wherein the sowing characteristic values ​​include seed planting density values.

[0167] Example 19 is an agricultural operating machine, comprising:

[0168] A communication system that receives a priori vegetation index map, the priori vegetation index map indicating vegetation index values ​​corresponding to different geographical locations in the field;

[0169] A geolocation sensor that detects the geolocation of agricultural machinery;

[0170] A field sensor that detects crop state characteristics corresponding to the geographical location;

[0171] A prediction model generator generates a predictive crop state model based on the vegetation index value at the geographical location in the prior vegetation index map and the crop state characteristics sensed by the field sensors at the geographical location. The predictive crop state model models the relationship between the first agricultural characteristic value and the crop state characteristics.

[0172] A prediction map generator generates a functional predicted crop state map of the field based on the vegetation index values ​​in the prior vegetation index map and the predicted crop state model. The functional predicted crop state map maps the predicted crop state values ​​to different locations in the field.

[0173] Example 20 is an agricultural operating machine of any or all of the foregoing examples, wherein the field sensors include optical sensors, and wherein a geolocation sensor detects a geolocation at a given time after the field sensors detect crop state characteristics corresponding to the geolocation, the length of which is at least in part based on the machine speed and the field of view of the optical sensors.

[0174] Although the subject matter has been described in language specific to structural features or methodological actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are disclosed as examples of the claims.

Claims

1. An agricultural operating machine (100), comprising: A communication system (206) receives an information map (258) which includes values ​​of a first agricultural characteristic corresponding to different geographical locations in the field; A geolocation sensor (204) detects the geolocation of the agricultural machinery; A field sensor (208) detects crop status corresponding to the geographical location as a second agricultural characteristic; A predictive model generator (210) generates a predictive agricultural model based on the value of the first agricultural characteristic in the information map (258) at the geographic location and the value of the second agricultural characteristic sensed by the field sensor (208) at the geographic location, the predictive agricultural model modeling the relationship between the first agricultural characteristic and the second agricultural characteristic; and A prediction map generator (212) generates a functional predictive agricultural map of the field based on the value of a first agricultural characteristic in the information map (258) and the predictive agricultural model, the functional predictive agricultural map mapping the predicted value of the second agricultural characteristic to the different geographical locations in the field.

2. The agricultural machinery according to claim 1, wherein, The prediction map generator configures the functional predictive agricultural map for use by the control system, which generates control signals based on the functional predictive agricultural map to control the controllable subsystems on the agricultural machinery.

3. The agricultural machinery according to claim 1, wherein, The field sensor generates sensor data indicating the crop status, and the field sensor includes: A processing system configured to analyze the sensor data and determine at least one of the orientation or magnitude of the crop state.

4. The agricultural machinery according to claim 1, wherein, The information map includes a priori vegetation index map, which maps the vegetation index value, which is the first agricultural characteristic, to the different geographical locations in the field.

5. The agricultural machinery according to claim 4, wherein, The predictive model generator is configured to determine the relationship between the crop state detected at the geographic location and the vegetation index value at the geographic location in the vegetation index value in the prior vegetation index map. The predictive agriculture model is configured to receive input vegetation index values ​​as model input and generate predicted crop state values ​​as model output based on the determined relationship.

6. The agricultural machinery according to claim 1, wherein, The information map includes a sowing characteristic map, which maps the sowing characteristic values, which are the first agricultural characteristics, to the different geographical locations in the field.

7. The agricultural machinery according to claim 6, wherein, The predictive model generator is configured to determine the relationship between the crop state detected at the geographic location and the sowing characteristics in the sowing characteristic map at the geographic location. The predictive agricultural model is configured to receive input sowing characteristic values ​​as model input and generate predicted crop state values ​​as model output based on the determined relationship.

8. The agricultural machinery according to claim 7, wherein, The sowing characteristics include hereditary stem or stalk strength or hereditary susceptibility to lodging.

9. A computer-implemented method for generating functional predictive agricultural maps, the computer-implemented method comprising: Receive an information map (258) at the agricultural machinery, the information map indicating the value of a first agricultural characteristic corresponding to different geographical locations in the field; Detect the geographical location of the agricultural machinery; The crop status corresponding to the geographical location is detected using field sensors (208) as a second agricultural characteristic; Generate a predictive agricultural model that models the relationship between the first agricultural characteristic and the second agricultural characteristic; and A predictive map generator (212) is controlled to generate a functional predictive agricultural map of the field based on the value of the first agricultural characteristic in the information map (258) and the predictive agricultural model, the functional predictive agricultural map mapping the predicted value of the second agricultural characteristic to the different geographical locations in the field.

10. An agricultural operating machine (100), comprising: A communication system (206) receives a priori vegetation index map (332), which indicates vegetation index values ​​corresponding to different geographical locations in the field; A geolocation sensor (204) detects the geolocation of the agricultural machinery; A field sensor (208) detects crop state characteristics corresponding to the geographical location; A prediction model generator (210) generates a prediction crop state model based on the vegetation index value at the geographical location in the prior vegetation index map (332) and the crop state characteristics at the geographical location sensed by the field sensor (208). The prediction crop state model models the relationship between the first characteristic value and the crop state characteristics. and A prediction map generator (212) generates a functional prediction crop state map of the field based on the vegetation index values ​​in the prior vegetation index map (332) and the prediction crop state model. The functional prediction crop state map maps the prediction crop state values ​​to the different geographical locations in the field.

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