Graph generation and control system

Abstract

One or more information maps are obtained by an agricultural work machine. The one or more information maps map values of one or more agricultural properties at different geographic locations of a field. Onboard sensors on the agricultural work machine sense the agricultural properties as the agricultural work machine moves through the field. A prediction map generator generates a prediction map of predicted agricultural properties at different locations in the field based on relationships between values in the one or more information maps and the agricultural properties sensed by the onboard sensors. The prediction map 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] A variety of different conditions in the field can have a number of adverse effects on harvesting operations. Therefore, when this situation is encountered during harvesting operations, the operator may try to modify the controls of the harvester.

[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] 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.

[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 2 The 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] Figures 4A-4B (Referred to as Figure 4 in this document) is a block diagram illustrating examples of a prediction model generator and a prediction graph generator.

[0011] Figures 5A-5D (Hereinafter referred to as Figure 5) is a flowchart illustrating an exemplary operation for controlling an 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 in agricultural harvesters and the architecture shown in the previous figure. 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 concurrently with agricultural operations, and more specifically, generating functional prediction crop state maps, crop height maps, and header characteristic maps. In some examples, functional prediction crop state maps, crop height maps, and header characteristic maps can be used to control agricultural machinery, such as combine harvesters. Unless machine settings also change, the performance of combine harvesters may degrade when they enter areas with variations in crop state, crop height, and terrain.

[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 early in the growing season or in the middle of the growing season, it can indicate the progress of crop plant development. 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 and plant health status.

[0019] Seeding characteristic maps exemplarily 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 have been planted. This information can be used to determine seed planting density in relation to plant density. For example, plant density affects the crop's resistance to lodging due to wind. Seeding characteristic maps may also include other information, such as the characteristics of the seeds used. Examples of characteristics include seed type, genetic stem strength, sensitivity to genetic green mutations, seed brand, seed coat, planting date, germination period, typical growth period, mature plant height, and seed hybridization.

[0020] Topographic maps illustratively map the elevation of the ground at different geographic locations within a field of interest. Since ground slope indicates changes in elevation, having two or more elevation values ​​allows for the calculation of the slope across areas with known elevation values. Larger slope interval sizes can be achieved by including more areas with known elevation values. When an agricultural harvester traverses the terrain in a known direction, the harvester's pitch and roll can be determined based on the ground slope (i.e., the area of ​​elevation change). When mentioned below, terrain characteristics may include, but are not limited to, elevation, slope (e.g., including the machine's orientation relative to the slope), and ground profile (e.g., roughness).

[0021] This discussion also includes predictive maps that predict features based on infographics and their relationship with sensed data obtained from field sensors. Two types of these predictive maps include a predicted yield map and a predicted biomass map. 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, a soil type map, a soil composition map, or a soil health map.

[0022] Therefore, this discussion is directed to an example in which the system receives one or more of a vegetation index map, a seeding map, a topographic map, a predicted yield map, or a predicted biomass map during harvesting operations, and uses field sensors that detect variables indicating crop status, crop height, or header characteristics. The system generates a model that models the relationship between vegetation index values, seeding characteristic values, topographic characteristic values, predicted yield values, or predicted biomass values ​​from one or more received maps and field data from field sensors, where the field data represents variables indicating crop status, crop height, and header characteristics. This model is used to generate a functional prediction map that predicts crop status, crop height, and header characteristics in the field. The functional prediction map generated during harvesting operations can be presented to an operator or other user for automatic control of the agricultural harvester during harvesting operations, or both.

[0023] Figure 1This 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 fogging machines 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.

[0024] 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 1 As 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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 the harvester 100. In another example, the crop state sensor may be positioned on the harvester 100 and viewed in one or more directions other than in front of the 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 harvester 100, such as, but not limited to, “sideways,” “towards the machine,” “away from the machine,” or “random orientation.” Some orientations may be absolute (e.g., relative to the Earth), such as digital compass heading or digital deviation from gravity or surface perpendicularity. For example, in some cases, the bearing can be provided relative to magnetic north, relative to true north, relative to the crop row, relative to the harvester's heading, or relative to other references.

[0036] In another example, the crop status or crop height 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 items, it can also indicate lodged crop, the extent of lodged crop condition, or the orientation of lodged crop.

[0037] Before describing how the agricultural harvester 100 generates a functional predictive crop state map and a header height map, and uses the functional predictive crop state map and header height map for control, a brief description of some items on the agricultural harvester 100 and their corresponding operations will be given 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.

[0038] In reference Figure 2 Following the description of the general method in Figure 3, a more specific method for generating a functionally predicted crop status map and a header height map is described with reference to Figures 4 and 5. This functionally predicted weed map 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.

[0039] 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.

[0040] 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 scenario information. Scenario information may include, but is not limited to, one or more weather conditions throughout the growing season or a period of the growing season, the presence of pests, geographical location, soil properties, irrigation, treatment application, etc. Weather conditions may include, but are not limited to, precipitation throughout the season, the presence of hail that can damage crops, wind direction, temperature throughout the season, etc. Some examples of pests broadly include insects, fungi, weeds, bacteria, viruses, etc. Some examples of treatment application include herbicides, insecticides, 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.

[0041] 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.

[0042] 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.

[0043] 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, such as a forward-looking monocular or stereo camera system and image processing system, or a camera mounted to view crop plants adjacent to the agricultural harvester 100, excluding those 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.

[0044] 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 sense 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 prediction map 263, which predicts the crop status at different locations in the field. Predictive map generator 212 therefore outputs predictive map 264.

[0045] 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, 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, which predicts the values ​​of crop status at different locations in the field as sensed by Field Sensor 208. 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 the crop status at different locations in the field. Prediction Map Generator 212 then outputs Prediction Map 264.

[0046] 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.

[0047] like Figure 2As shown, prediction map 264 uses information values ​​from information map 258 at various locations on the field (or locations with similar scenario information, even in different fields) and prediction model 350 to predict the values ​​of characteristics sensed by field sensors 208 or the values ​​of characteristics related to characteristics sensed by field sensors 208 at these locations. 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 the prediction model are used to generate prediction map 264.

[0048] 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.

[0049] In some examples, the data type in Infographic 258 differs from the data type sensed by Field Sensor 208, but the data type in Predictive Infographic 264 is the same as that 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 state. Predictive Infographic 264 could be a predicted crop state map mapping 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. Predictive Infographic 264 could be a predicted crop height map mapping predicted crop height values ​​to different geographic locations in the field.

[0050] 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 Predictive 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 could be a predicted biomass map mapping predicted biomass 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 state. Predictive Graph 264 could be a predicted speed map mapping predicted harvester speed values ​​to different geographic locations in the field.

[0051] In some examples, Infographic 258 originates from a previous pass through the field 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 mix 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.

[0052] In some examples, Infographic 258 is derived from a field previously traversed during a priori operation, and the 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 harvesting operation. The predictive model is then used by prediction graph generator 212 to generate the predicted yield map.

[0053] In another example, Infographic 258 could be a weed intensity map generated during operation (e.g., 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, the weed intensity map at spraying time is recorded in a georeferenced manner as Infographic 258 and provided to agricultural harvester 100. Field sensor 208 can detect weed intensity at geographic locations in the field, and prediction model generator 210 can then build a prediction 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.

[0054] 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 accordingly.

[0055] 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.

[0056] 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.

[0057] 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 by, for example, correcting crop state values ​​displayed on the maps based on, for example, 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, recess 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 with lodged crop 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 gathered. The header and reel controller 238 can generate control signals to control the header or reel or other header functions. For example, in areas with lodged crops, it is beneficial to adjust the header height or reel position. 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.

[0058] 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.

[0059] At 280, the agricultural harvester 100 receives information map 258. Examples of information map 258 or receiving information map 258 are discussed with respect to boxes 282, 284, 285, and 286. As discussed above, as shown in box 282, information map 258 maps the values ​​of variables corresponding to a first characteristic to different locations in the field. As shown in box 281, receiving information map 258 may include selecting one or more of a plurality of possible information maps available. For example, one information map may be a vegetation index map generated from aerial imagery. Another information map 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 information maps may be manual, semi-automatic, or automatic. Information map 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, or the data may be measurements acquired during the previous year, early in the current growing season, or at other times. Information may also be based on data detected in other ways (besides using aerial imagery). For example, information diagram 258 can be transmitted to agricultural harvester 100 using communication system 206 and stored in data storage device 202.

[0060] Infographic 258 can also be a predictive map, as indicated by box 285. As described above, a predictive map may 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 may be based wholly or partially on a topographic map, soil type map, soil composition map, or soil health map. Predictive yield maps or predicted biomass maps may also be predicted and generated in other ways.

[0061] Infographic 258 can also be loaded onto the agricultural harvester 100 in other ways using communication system 206, as indicated by box 286 in the flowchart of Figure 3. In some examples, Infographic 258 can be received by communication system 206.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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 the predicted values ​​on prediction map 264 or the 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, the 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. The user permission level may be used to determine which display markers 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, an administrator, such as a supervisor 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 administrator, 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.

[0068] 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.

[0069] 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.

[0070] 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 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, a direction opposite to the crop's tilt direction can be maintained. 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 prediction graph 264 maps a crop state in front of the machine where the crop is lodged along one part of the header but not along another part, or if the lodging along one part of the header is more severe than along the other, the header can be controlled to tilt, lateralize, or both to collect lodged crop more efficiently. The examples for feed rate and header control using a functional prediction crop state graph are provided above as examples only. Therefore, values ​​obtained from the prediction crop state graph or other types of functional prediction graphs can be used to generate a variety of other control signals to control one or more of the controllable subsystems 216.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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, 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] Figures 4A-4B In this paper, they are collectively referred to as Figure 4. Figure 4A yes Figure 1 A block diagram of a portion of an agricultural harvester 100 is shown. Specifically, Figure 4A Examples of the prediction model generator 210 and the prediction graph generator 212 are shown in more detail. Figure 4A The diagram also illustrates the information flow between the different components. 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 the planted seeds. Furthermore, seed characteristics may include seed type, genetic stem or stem strength, genetic susceptibility to lodging, seed coat, seed genotype, planting date, seed growth period, etc.

[0083] 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] .

[0084] 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 343 can also be generated in other ways.

[0085] 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 sensors 208 schematically include an airborne crop status sensor, a crop height sensor 337, and a processing system 338. The processing system 338 processes the sensor data generated from the airborne crop status sensor 336 or the crop height sensor 337.

[0086] 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 as it moves through the field during harvesting operations. The processing system 338 processes one or more images acquired via the onboard crop condition sensor 336 to generate processed image data identifying one or more characteristics of the crop plants in the images. For example, the magnitude and orientation of the crop 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 at the time of receiving a signal from the field sensor 208 is typically not the exact location of the sensed crop condition. This is because it takes time for the harvester 100 (equipped with a geolocation sensor) to come into contact with the crop plant whose crop condition is being sensed. In some examples, to take forward sensing into account, 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.

[0087] 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 located on the agricultural harvester 100 or at a remote location.

[0088] This discussion focuses on an example in which the airborne crop state sensor 336 includes an optical sensor such as a camera. It should be understood that this is merely an example, and other examples of the sensors described above, as well as airborne crop state sensors 336, are considered herein. Figure 4A As shown, the prediction model generator 210 includes a vegetation index to crop state model generator 342, a sowing to crop state model generator 344, a yield to crop state model generator 345, and a biomass to crop state model generator 346. In other examples, compared to Figure 4AThe predictive model generator 210 may include additional, fewer, or different components than those shown in the examples. 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 or crop height models. For example, model generator 210 may include a yield-to-crop-height model generator and a biomass-to-crop-height model generator, respectively, to generate the relationship between crop height and yield and biomass. The crop height map generator 353 can use the models generated by these other model generators to generate a predicted crop height map based on values ​​from the predicted yield map 335 and the predicted biomass map 337.

[0089] Model generator 342 identifies the relationship between the field crop state data 340 at a geographic location corresponding to the location where the field crop state data 340 is geolocated, and the vegetation index values ​​from vegetation index map 332 corresponding to the same geographic location where the crop state data 340 is geolocated 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 vegetation index values ​​of georeferenced locations in the field contained in vegetation index map 332. In some examples, model generator 342 can use the time series of vegetation index maps to identify crop senescence rate after greensnap, increased crop stress from stem damage, etc.

[0090] Model generator 344 identifies the relationship between the crop state represented in the field crop state data 340 at a geographic location corresponding to the location where the field crop state data 340 is geographically located, and the sowing characteristic value at the same location. The sowing characteristic value is a geographic reference value included in the sowing characteristic map 33. Model generator 344 generates a predictive crop state model based on the sowing characteristic 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 sowing characteristic values ​​of the geographic references included in the sowing characteristic map 333 at different locations in the field. Sowing characteristics may be, for example, seed planting density.

[0091] Model generator 345 identifies the relationship between the crop state represented in the field crop state data 340 at a geographic location corresponding to the location where the field crop state data 340 is geolocated, and the predicted yield at the same location. The predicted yield value is a geographic reference value included in the predicted yield map 335. Model generator 345 generates a predicted crop state model for the crop state at a predicted location in the field based on the predicted yield value. The crop state model is used to predict the crop state at the same location in the field based on the predicted yield values ​​of the geographic references included at different locations in the field in the predicted yield map 335.

[0092] Model generator 346 identifies the relationship between the crop state represented in the field crop state data 340 at a geographic location corresponding to the location where the field crop state data 340 is geolocated, and the predicted biomass at the same location. The predicted biomass value is a geographic reference value included in the predicted biomass map 337. Model generator 346 generates a predicted crop state model for the crop state at a predicted location in the field based on the predicted biomass values. The crop state model is used to predict the crop state at the same location in the field based on the predicted biomass values ​​of the geographic reference included in the predicted biomass map 337 at different locations in the field.

[0093] In light 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 prediction crop state models described above 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 of these crop state models or combinations thereof in Figure 4A The dynamic model 350 is used to represent this.

[0094] The crop state prediction model 350 is provided to the prediction map generator 212. Figure 4A In one example, the prediction graph generator 212 includes a crop state graph generator 352. In other examples, the prediction graph generator 212 may include additional, fewer, or different graph generators. Therefore, the prediction graph generator 212 may include other graph generators 358. These other graph generators may include… Figure 4A and Figure 4BThe combination of graph generators in the process. Crop state graph generator 352 receives a predictive crop state model 350, which predicts crop state based on the relationship between sensed crop state values ​​and values ​​at locations corresponding to the sensed crop state from one or more of the vegetation index graph 332, seeding characteristic graph 333, predicted yield graph 335, and predicted biomass graph 337.

[0095] The crop state map generator 354 can also generate a functional predictive crop state map 360 based on vegetation index values, sowing characteristic values, predicted yield values, or predicted biomass values ​​at different locations in the field, as well as the predictive crop state model 350. This functional predictive crop state map predicts the crop state at said locations in the field. The generated functional predictive crop state map 360 can be provided to a control region generator 213, a 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 produce 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 an operator 260 or another user, 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 controllable subsystems 216.

[0096] like Figure 4A As shown, the prediction model generator 210 also includes a vegetation index to crop height model generator 347 and a seeding to crop height model generator 344.

[0097] Model generator 347 identifies the relationship between the field crop height data 340 at a geographic location corresponding to the location where the field crop height data 340 is geolocated, and the vegetation index value corresponding to the same location where the crop height data 340 is geolocated in the field from vegetation index map 332. Based on this relationship established by model generator 347, model generator 347 generates a crop height prediction model 351. Crop height model 351 is used to predict the crop height at the same location in the field based on the vegetation index values ​​of georeferenced locations in the field included in vegetation index map 332.

[0098] Model generator 349 identifies the relationship between the crop height represented in the field crop height data 340 at a geographic location corresponding to the location where the field crop height data 340 is geolocated, and the sowing characteristic value at the same location. The sowing characteristic value is a geographic reference value included in the sowing characteristic map 333. Model generator 349 generates a predicted crop height model based on the sowing characteristic value, which predicts the crop height at a location in the field. Crop height model 351 is used to predict the crop height at the same location in the field based on the sowing characteristic values ​​of the geographic reference at different locations in the field included in the sowing characteristic map 333.

[0099] The crop height prediction model 351 is provided to the prediction map generator 212. Figure 4A In the example, the prediction map generator 212 includes a crop height map generator 353. The crop height map generator 353 receives a predicted crop height model 351, which predicts crop height based on the relationship between sensed crop height values ​​and values ​​from one or more of the vegetation index map 332 and the sowing characteristic map 333 at locations corresponding to the locations where the crop height is sensed.

[0100] The crop height map generator 353 can also generate a functional predicted crop height map 361 based on vegetation index values ​​or sowing characteristic values ​​at different locations in the field and a predicted crop height model 351. This functional predicted crop height map predicts the crop height at those locations in the field. The generated functional predicted crop height map 361 can be provided to a control area generator 213, a control system 214, or both. The control area generator 213 generates control areas and incorporates these control areas into the functional prediction map, i.e., into the prediction map 361, to produce a predicted control area map 265. One or both of the functional prediction map 264 or the predicted control area map 265 can be presented to an operator 260 or another user, or provided to the control system 214, which generates control signals based on the prediction map 264, the predicted control area map 265, or both, to control one or more controllable subsystems 216.

[0101] Figure 4B yes Figure 1 A block diagram of a portion of an agricultural harvester 100 is shown. Specifically, Figure 4B Examples of the prediction model generator 210 and the prediction graph generator 212 are shown in more detail. Figure 4B The diagram also illustrates the information flow between the various components shown. The prediction model generator 210 receives the topographic map 432 as an information map. The topographic map 432 includes geographic reference topographic characteristic values.

[0102] Generator 210 also receives a geographic location indicator 434 from geographic location sensor 204. Field sensors 208 illustratively include operator input sensors (e.g., operator input sensor 436) and processing system 438. Operator input sensor 436 senses various operator inputs, such as setting inputs for controlling the settings of one or more components of the agricultural harvester 100, such as header height settings or header tilt settings controlling the height, orientation (tilt and yaw) of the header or its wing components. Processing system 438 processes the sensor data generated by operator input sensor 436 to generate processed sensor data 440, some examples of which are described below.

[0103] This discussion is based on the following example, in which operator input sensor 436 senses header setting inputs for controlling settings of the header 102 on an agricultural harvester 100, such as header height setting or header orientation setting. Figure 4B As shown, the exemplary prediction model generator 210 includes one or more of a terrain feature to cutter height model generator 442 and a terrain feature to cutter orientation model generator 444. In other examples, the prediction model generator 210 may include more than Figure 4B The examples show more, fewer, or different components. Therefore, in some examples, the prediction model generator 210 may also include additional items 448, which may include other types of prediction model generators for generating other types of dynamic models. For example, the prediction model generator 210 may include a specific terrain characteristic model generator, such as a slope-to-cut height model generator or a slope-to-cut orientation model generator.

[0104] The terrain feature to header height model generator 442 identifies the relationship between the header height at a geographic location corresponding to the location where the operator input sensor 436 senses a header height setting indicating the header height, and the terrain feature values ​​(e.g., one or more slope values) corresponding to the same location in the field from the topographic map 432 that correspond to the sensed height data in the field. Based on this relationship established by the terrain feature to header height model generator 442, the terrain feature to header height model generator 442 generates a predicted header feature model 450. The prediction map generator 212 uses the predicted header feature model 450 to predict the header features (e.g., header height) at the same location in the field based on the terrain feature values ​​(e.g., slope values) of a geographic reference contained in the topographic map 432 at different locations in the field.

[0105] The terrain feature to cutter azimuth model generator 444 identifies the relationship between the cutter azimuth at a geographic location corresponding to the location where the operator input sensor 436 senses the cutter azimuth setting indicating the cutter azimuth, and terrain feature values ​​(e.g., one or more slope values) from the topographic map 432 corresponding to the same location in the field where the cutter azimuth was sensed. Based on this relationship established by the terrain feature to cutter azimuth model generator 444, the terrain feature to cutter azimuth model generator 444 generates a predicted cutter feature model 450. The prediction map generator 212 uses the predicted cutter feature model 450 to predict the cutter features (e.g., cutter azimuth) at the same location in the field based on the terrain feature values ​​(e.g., slope values) of a geographic reference contained in the topographic map 432 at different locations in the field.

[0106] In view of the above, the prediction model generator 210 is operable to generate multiple prediction header characteristic models, such as one or more prediction header characteristic models generated by model generators 442, 444, and 448. In another example, two or more of the above-described prediction header characteristic models can be combined into a single prediction header characteristic model, which predicts two or more header characteristics, such as header height and header orientation, based on different values ​​at different locations in the field. Any one or a combination of these header characteristic models is generated by... Figure 4B The dynamic model 450 in the text is represented by the same symbol.

[0107] The predicted chute characteristic model 450 is provided to the prediction map generator 212. Figure 4B In one example, the prediction graph generator 212 includes a chute height graph generator 452 and a chute orientation graph generator 454. In other examples, the prediction graph generator 212 may include additional, fewer, or different graph generators. Thus, in some examples, the prediction graph generator 212 may include additional items 458, which may include other types of graph generators for generating chute characteristic graphs for other types of chute characteristics.

[0108] The header height map generator 452 receives the predicted header characteristic model 450 and generates a predicted map that maps the predicted height of the header at different locations in the field. The predicted header characteristic model predicts the header height based on values ​​in the topographic map 432 and field sensor data indicating the header height.

[0109] The header orientation map generator 454 receives the predicted header characteristic model 450 and generates a predicted map that maps the predicted orientation of the header at different locations in the field. The predicted header characteristic model predicts the header orientation based on values ​​in the topographic map 432 and field sensor data indicating the header orientation.

[0110] Prediction map generator 212 outputs one or more functional prediction header characteristic maps 460, which predict one or more header characteristics, such as header height or header orientation. Each of the prediction header characteristic maps 460 predicts the header characteristics at different locations in the field. Each of the generated prediction header characteristic maps 460 can be provided to control region generator 213, control system 214, or both. Control region generator 213 generates control regions and incorporates these control regions into the functional prediction maps, i.e., into prediction maps 460, to produce prediction control region maps 265. One or both of prediction maps 264 and prediction control region maps 265 can be provided to control system 214, which generates control signals based on prediction maps 264, prediction control region maps 265, or both to control one or more controllable subsystems 216.

[0111] Figure 5A This is a flowchart illustrating an example of the operation 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 562, the prediction model generator 210 and the prediction map generator 212 receive one or more of the vegetation index map 332, the seeding characteristic map 333, the prediction yield map 335, and the prediction biomass map 337.

[0112] At box 563, 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 can change which infographic is being used when it detects that one of the other candidate 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 if the seeding characteristic infographic 333 is better related to the crop state sensed by the field sensors.

[0113] At frame 564, a crop status sensor signal is received from the airborne crop status sensor 336. As described above, the airborne crop status sensor 336 may be an optical sensor 565 or some other crop status sensor 570.

[0114] At box 572, the processing system 338 processes one or more 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.

[0115] At box 582, 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, since the current field crop state sensor signal is sensed in an image taken in front of the harvester 100, the current field crop state sensor signal corresponds to the location in the field in front of the harvester 100. This is indicated by box 578.

[0116] At box 584, 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, or predicted yield values ​​obtained from an infographic (e.g., 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, which may also indicate a tall crop plant population.

[0117] At box 586, a predicted crop state model (e.g., predicted crop state model 550) is provided to a prediction map generator 212, which generates a functional predicted crop state map 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. This functional predicted crop state map maps the predicted crop state to different geographic locations in the field. 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 agricultural harvesting operations. Thus, the functional predicted crop state map 360 is generated as an agricultural harvester moves through a field where an agricultural harvesting operation is being performed.

[0118] At box 594, the prediction map generator 212 outputs a functional predicted crop state map 360. At box 593, the prediction map generator 212 configures the functional predicted crop state map 360 for use by the control system 214. At box 595, the prediction map generator 212 can also provide map 360 to the control region generator 213 to generate a control region. At box 597, the prediction map generator 212 further 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.

[0119] Figure 5B This is a flowchart illustrating an example of the operation of the prediction model generator 210 and the prediction map generator 212 in generating a prediction crop height model 351 and a functional prediction crop height map 361. In box 1562, the prediction model generator 210 and the prediction map generator 212 receive one or more of a priori vegetation index map 332 and a sowing characteristic map 333.

[0120] At box 1563, 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 can change which infographic is being used when other candidate infographics are detected to be more closely related to the field-sensed crop height. 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 height sensed by the field sensors.

[0121] At frame 1564, a crop height sensor signal is received from the airborne crop height sensor 337. As described above, the airborne crop height sensor 336 may be an optical sensor 565 or some other crop height sensor 1570.

[0122] At frame 1572, the processing system 338 processes one or more field sensor signals received from the onboard crop height sensor 336 to generate a crop height value that indicates the crop height of crop plants in the field near the agricultural harvester 100.

[0123] At box 1582, the predictive model generator 210 also obtains the geographic location corresponding to the sensor signal. For example, the predictive 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 height 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 height sensor signal is captured does not typically correspond to the crop height of the crop at the current geographic location of the harvester 100. Instead, since the current field crop height sensor signal is sensed in an image taken in front of the harvester 100, the current field crop height sensor signal corresponds to the location in the field in front of the harvester 100. This is indicated by box 1578.

[0124] At box 1584, prediction model generator 210 generates one or more prediction crop height models, such as crop height model 351, which model the relationship between at least one of vegetation index values ​​and sowing characteristic values ​​obtained from an infographic (e.g., infographic 258) and crop height sensed by field sensor 208. For example, prediction model generator 210 may generate prediction crop height models based on sowing density values ​​and sensed crop height indicated by sensor signals obtained from field sensor 208, which may also indicate a tall crop population.

[0125] At box 1586, a predicted crop height model (e.g., predicted crop height model 351) is provided to a prediction map generator 212, which generates a functional predicted crop height map based on a prior vegetation index map 332, a seeding characteristic map 333, a predicted yield map 335 or a predicted biomass map 337, and the predicted crop height model 351. This functional predicted crop height map maps the predicted crop height to different geographic locations within the field. For example, in some examples, the functional predicted crop height map 361 predicts crop height. In other examples, the functional predicted crop height map 361 predicts other items. Furthermore, the functional predicted crop height map 361 can be generated during agricultural harvesting operations. Thus, the functional predicted crop height map 361 is generated as an agricultural harvester moves through the field where an agricultural harvesting operation is being performed.

[0126] At box 1594, the prediction map generator 212 outputs a functional predicted crop height map 361. At box 1593, the prediction map generator 212 configures the functional predicted crop height map 361 for use by the control system 214. At box 1595, the prediction map generator 212 can also provide map 361 to the control region generator 213 to generate a control region. At box 1597, the prediction map generator 212 further configures map 361 in other ways. The functional predicted crop height map 361 (with or without a control region) is provided to the control system 214.

[0127] Figure 5C This is a flowchart illustrating an example of the operation of the prediction model generator 210 and the prediction map generator 212 in generating a prediction chute characteristic model 650 and a functional prediction chute characteristic map 460. At block 662, the prediction model generator 210 and the prediction map generator 212 receive a topographic map 432 or some other map 663. At block 664, the processing system 638 receives one or more sensor signals from field sensors 208 (e.g., operator input sensor 436). In other examples, the field sensor 208 may be another type of sensor, as shown in block 670. For example, the field sensor 208 may be another type of sensor that provides an indication of chute characteristics, such as chute height or chute orientation.

[0128] At box 672, processing system 638 processes one or more received sensor signals to generate data indicating cutter characteristics. As shown in box 674, cutter characteristics may be cutter height. As shown in box 676, cutter characteristics may be cutter orientation. As shown in box 680, sensor data may indicate other cutter characteristics.

[0129] At box 682, the predictive model generator 210 also obtains the geographic location corresponding to the sensor data. For example, the predictive model generator 210 can obtain the geographic location from the geographic location sensor 204 and determine the precise geographic location of the captured or derived sensor data 640 based on machine latency, machine speed, etc.

[0130] At box 684, prediction model generator 210 generates one or more prediction models, such as cutter characteristic model 450, which model the relationship between terrain characteristic values ​​(e.g., slope values) obtained from an infographic (e.g., infographic 258) and cutter characteristics or related characteristics sensed by field sensors 208. For example, prediction model generator 210 may generate a predicted cutter characteristic model that models the relationship between terrain characteristic values ​​(e.g., slope) and sensed cutter characteristics (e.g., cutter height or cutter orientation) indicated by sensor data obtained from field sensors 208 (e.g., operator input sensor 436).

[0131] At box 686, a prediction model (e.g., a prediction header characteristic model) is provided to a prediction map generator 212, which generates a prediction header characteristic map 660 based on topographic map 432 and the prediction header characteristic model 450. This prediction header characteristic map maps the predicted header characteristics. For example, in some examples, the prediction header characteristic map 460 maps the predicted header height or predicted header orientation at various locations on the field. Furthermore, the prediction header characteristic map 460 can be generated during agricultural operations. Thus, as an agricultural harvester moves through the field where agricultural operations are performed, the prediction header characteristic map 460 is generated along with the execution of the agricultural operations.

[0132] At box 694, the prediction map generator 212 outputs a predicted cutter characteristic map 460. At box 691, the prediction map generator 212 outputs the predicted cutter characteristic map to be presented to operator 260 for possible interaction. At box 693, the prediction map generator 212 can configure the predicted cutter characteristic map for use by the control system 214. At box 695, the prediction map generator 212 can also provide the predicted cutter characteristic map 460 to the control area generator 213 to generate a control area. At box 697, the prediction map generator 212 also configures the predicted cutter characteristic map 460 in other ways. The predicted cutter characteristic map 460 (with or without a control area) is provided to the control system 214.

[0133] At box 696, Figure 5D In this process, the control system 214 generates control signals based on the predicted crop state diagram 360 and the predicted header characteristic diagram 460 to control the controllable subsystem 216.

[0134] As shown in box 671, the height of the reed can be adjusted. For example, the reed height can be lowered to collect lodged crop plants. For example, the reed height can be raised to better collect tall crop plants.

[0135] As shown in box 673, the fore-and-aft position of the reel can be adjusted. The reel can be moved forward, for example, to raise lodged crop in front of the cutter bar, thereby allowing the cutter bar to make better contact with the lodged crop. The reel can also be moved backward, for example, to help place the cut crop material onto the belt.

[0136] As shown in box 675, the header height can be adjusted. For example, the header can be raised or lowered to adapt to the terrain characteristics of the field. For example, the header can be lowered to better capture lodged crops.

[0137] As shown in box 677, the header orientation can be adjusted. The header can tilt left or right to, for example, follow terrain features. The header can tilt forward or backward to better collect crops or follow terrain features. In some examples, the header may include multiple components (sometimes called wings) that can be independently oriented and height controlled.

[0138] The controllable subsystem 216 can also be controlled in other ways, as shown in box 679.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] It should be noted that the foregoing discussion has described various different systems, components, logic, and interactions. It should be understood that any or all of such systems, components, logic, and interactions can be implemented by hardware items, such as processors, memory, or other processing units, that perform functions associated with those systems, components, logic, or interactions, as described below. Furthermore, any 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 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 or all of the systems, components, logic, and interactions described above. Other structures may also be used.

[0144] 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.

[0145] exist Figure 6 In the examples shown, some items are similar to Figure 2The 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 the 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.

[0158] Note that other forms of device 16 are possible.

[0159] Figure 10 It is one of the deployable ones Figure 2 An example of a computing environment for the components. Reference Figure 10An 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.

[0160] 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.

[0161] 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.

[0162] Computer 810 may also include other removable / non-removable volatile / non-volatile computer storage media. This is just one example. Figure 10 A 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).

[0163] 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.

[0164] 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 10In 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] Example 1 is an agricultural operating machine, comprising:

[0170] A communication system that receives one or more information maps, each of which includes values ​​for one or more agricultural characteristics corresponding to different geographical locations in the field;

[0171] A geolocation sensor that detects the geolocation of agricultural machinery;

[0172] At least one field sensor, said at least one field sensor detects a value of at least one agricultural characteristic corresponding to the geographical location;

[0173] A prediction map generator generates one or more functional predictive agricultural maps of a field based on the values ​​of one or more agricultural characteristics in one or more information maps and based on the value of at least one agricultural characteristic, the functional predictive agricultural maps mapping the predicted values ​​of the at least one agricultural characteristic to different geographical locations in the field;

[0174] Controllable subsystem; and

[0175] The control system generates control signals to control the controllable subsystem based on the geographical location of the agricultural machinery and the values ​​of the agricultural characteristics in the functional prediction agricultural map.

[0176] Example 2 is any or all of the agricultural operating machines of the foregoing examples, wherein the prediction map generator includes:

[0177] A predictive crop height map generator generates a functional predictive crop height map as one of the one or more functional predictive agriculture maps, which maps predicted crop heights to different geographic locations in the field.

[0178] Example 3 is any or all of the agricultural operating machines of the foregoing examples, wherein the prediction map generator includes:

[0179] A predictive crop state map generator generates a functional predictive crop state map as one of one or more functional predictive agriculture maps, which maps the predicted crop state to different geographical locations in the field.

[0180] Example 4 is any or all of the agricultural operating machines of the foregoing examples, wherein the prediction map generator includes:

[0181] A predictive header height map generator generates a functional predictive header height map as one of one or more functional predictive agriculture maps, which maps the predicted header height to different geographic locations in the field.

[0182] Example 5 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system includes:

[0183] A header controller that generates one or more header control signals indicative of header control based on detected geographic locations and one or more functional predictive agricultural maps, and controls a controllable subsystem based on one or more header control signals.

[0184] Example 6 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system controls the header height based on one or more header control signals.

[0185] Example 7 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system controls the pitch or tilt of the header based on one or more header control signals.

[0186] Example 8 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system controls the height of the reel based on one or more header control signals.

[0187] Example 9 is any or all of the agricultural machinery described in the preceding examples, wherein the control system controls the forward and backward position of the reel based on one or more header control signals.

[0188] Example 10 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system further includes:

[0189] An operator interface controller that generates a user interface diagram representation of at least one of the one or more functional predictive agricultural maps.

[0190] Example 11 is a computer-implemented method for controlling agricultural machinery, comprising:

[0191] Obtain a first information map, which contains values ​​of terrain features corresponding to different geographical locations in the field;

[0192] Obtain a second information map, which contains values ​​of the first agricultural characteristic corresponding to different geographical locations in the field;

[0193] Detecting the geographical location of agricultural machinery;

[0194] The values ​​of the second agricultural characteristic corresponding to the geographical location are detected using on-site sensors;

[0195] Based on the values ​​of terrain characteristics in the first information map, a first functional predictive agricultural map of the field is generated, which maps the predicted header height values ​​to different geographical locations in the field.

[0196] Based on the values ​​of the first and second agricultural characteristics in the second information map, a second functional predictive agricultural map of the field is generated. This second functional predictive agricultural map maps the predicted values ​​of the third agricultural characteristic to different geographical locations within the field; and

[0197] Based on the geographical location of the agricultural machinery, and based on the header height value predicted in the agricultural map by the first function, and the third agricultural characteristic value predicted in the agricultural map by the second function, one or more header controllable subsystems are controlled.

[0198] Example 12 is a computer-implemented method of any or all of the foregoing examples, wherein controlling the controllable subsystem includes:

[0199] Generate header height control signals based on detected geographic locations and second-function predictive agricultural maps; and

[0200] The controllable subsystem is controlled based on the header height control signal to control the header height.

[0201] Example 13 is a computer-implemented method of any or all of the foregoing examples, wherein generating the second feature prediction graph includes:

[0202] A functional crop state map is generated, which maps predicted crop state values ​​to different geographical locations in the field.

[0203] Example 14 is a computer-implemented method of any or all of the foregoing examples, wherein controlling the controllable subsystem includes:

[0204] Based on the detected geographic location and the first-function predicted agricultural map, a reel height control signal is generated; and

[0205] The controllable subsystem is controlled based on the reel height control signal to control the height of the reel.

[0206] Example 15 is a computer-implemented method of any or all of the foregoing examples, wherein controlling the controllable subsystem includes:

[0207] Based on the detected geographic location and the first-function predicted agricultural map, a reel position control signal is generated; and

[0208] The controllable subsystem is controlled based on the reel position control signal to control the front and rear position of the reel.

[0209] Example 16 is a computer-implemented method of any or all of the foregoing examples, wherein generating the second feature prediction graph includes:

[0210] Generate a functionally predicted crop height map, which maps predicted crop height values ​​to different geographic locations in the field.

[0211] Example 17 is a computer-implemented method of any or all of the foregoing examples, wherein controlling the controllable subsystem includes:

[0212] Based on the detected geographic location and the first-function predicted agricultural map, a reel position control signal is generated; and

[0213] The controllable subsystem is controlled based on the reel position control signal to control the front and rear position of the reel.

[0214] Example 18 is a computer-implemented method of any or all of the foregoing examples, wherein controlling the controllable subsystem includes:

[0215] Based on the detected geographic location and the first-function predicted agricultural map, a reel height control signal is generated; and

[0216] The controllable subsystem is controlled based on the reel height control signal to control the height of the reel.

[0217] Example 19 is an agricultural operating machine, comprising:

[0218] A communication system that receives an information map, the information map including values ​​of agricultural characteristics corresponding to different geographical locations in the field;

[0219] A geolocation sensor that detects the geolocation of agricultural machinery;

[0220] A field sensor that detects one or more values ​​of cutter height, crop status, and crop height as a second agricultural characteristic corresponding to the geographical location;

[0221] A predictive model generator generates a predictive agricultural model based on the values ​​of an agricultural characteristic in an information map at the geographic location and the values ​​of a second agricultural characteristic sensed by field sensors at the geographic location, the predictive agricultural model modeling the relationship between the agricultural characteristic and the second agricultural characteristic;

[0222] A predictive map generator that generates a functional predictive agriculture map of a field based on the values ​​of agricultural characteristics in an information map and a predictive agriculture model, the functional predictive agriculture map mapping control values ​​to different geographic locations in the field.

[0223] Controllable subsystem; and

[0224] A control system that generates control signals based on the geographical location of agricultural machinery and control values ​​in a functional predictive agricultural map to control a controllable subsystem.

[0225] Example 20 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system includes a header controller that controls one or more of the header height and reel position based on control values.

[0226] 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: Communication system (206), the communication system receiving one or more information maps, each information map including one or more agricultural characteristics corresponding to different geographical locations in the field; A geolocation sensor (204) detects the geolocation of the agricultural machinery; At least one field sensor (208) detects a value of at least one agricultural characteristic corresponding to the geographical location; A prediction map generator (212) generates one or more functional predictive agricultural maps of the field based on the values ​​of one or more agricultural characteristics in the one or more information maps and based on the values ​​of at least one agricultural characteristic, the one or more functional predictive agricultural maps mapping the predicted values ​​of the at least one agricultural characteristic to the different geographical locations in the field; Controllable subsystem (216); and A control system (214) generates control signals based on the geographical location of the agricultural machine (100) and the values ​​of agricultural characteristics in the functional prediction agricultural map to control the controllable subsystem (216).

2. The agricultural machinery according to claim 1, wherein, The prediction map generator includes: A predicted crop height map generator generates a functional predicted crop height map as one of the one or more functional predicted agricultural maps, which maps the predicted crop height to the different geographical locations in the field.

3. The agricultural machinery according to claim 2, wherein, The prediction map generator includes: A predictive crop state map generator generates a functional predictive crop state map as one of the one or more functional predictive agricultural maps, which maps the predicted crop state to the different geographical locations in the field.

4. The agricultural machinery according to claim 3, wherein, The prediction map generator includes: A predictive header height map generator generates a functional predictive header height map as one of the one or more functional predictive agricultural maps, the functional predictive header height map mapping the predicted header height to the different geographical locations in the field.

5. The agricultural machinery according to claim 4, wherein, The control system includes: A header controller that generates one or more header control signals indicating one or more header controls based on the detected geographic location and one or more functional predictive agricultural maps, and controls the controllable subsystem based on the one or more header control signals.

6. The agricultural machinery according to claim 5, wherein, The control system controls the height of the cutting platform based on one or more cutting platform control signals.

7. The agricultural machinery according to claim 6, wherein, The control system controls the pitch or tilt of the cutter head based on one or more cutter head control signals.

8. The agricultural machinery according to claim 7, wherein, The control system controls the height of the reel based on the one or more header control signals.

9. A computer-implemented method for controlling agricultural machinery (100), comprising: Obtain a first information map (258), the first information map containing values ​​of terrain features corresponding to different geographical locations in the field; Obtain a second information map (258), the second information map containing values ​​of the first agricultural characteristic corresponding to the different geographical locations in the field; Detect the geographical location of the agricultural machinery (100); The value of the second agricultural characteristic corresponding to the geographical location is detected using a field sensor (208); Based on the values ​​of terrain characteristics in the first information map, a first functional predictive agricultural map of the field is generated, which maps the predicted header height values ​​to the different geographical locations in the field. Based on the value of the first agricultural characteristic in the second information map and based on the value of the second agricultural characteristic, a second functional predictive agricultural map of the field is generated, and the second functional predictive agricultural map maps the predicted value of the third agricultural characteristic to the different geographical locations in the field; and Based on the geographical location of the agricultural machine (100), and based on the header height value in the agricultural map predicted by the first function, and based on the third agricultural characteristic value in the agricultural map predicted by the second function, one or more header controllable subsystems (216) are controlled.

10. An agricultural operating machine (100), comprising: A communication system (206) receives an information map (258) comprising agricultural characteristics corresponding to different geographical locations in the field; A geolocation sensor (204) detects the geolocation of the agricultural machinery (100); A field sensor (208) detects values ​​of one or more of the following as second agricultural characteristics—crop height, crop status, and crop height—corresponding to the geographical location; A predictive model generator (210) generates a predictive agricultural model based on the value of the agricultural characteristic in the information map 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 agricultural characteristic and the second agricultural characteristic; A prediction map generator (212) generates a functional predictive agriculture map of the field based on the values ​​of the agricultural characteristics in the information map (258) and the predictive agriculture model, the functional predictive agriculture map mapping control values ​​to the different geographical locations in the field; Controllable subsystem (216); and A control system (214) generates control signals based on the geographical location of the agricultural machine (100) and control values ​​in the functional prediction agricultural map to control the controllable subsystem (216).

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