Machine control using prediction maps

Abstract

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural property values at different geographic locations of a field. An on-board sensor on the agricultural work machine senses an agricultural property as the agricultural work machine moves through the field. A prediction map generator generates a prediction map based on a relationship between the values in the one or more information maps and the agricultural property sensed by the on-board sensor, the prediction map predicting a predicted agricultural property at different locations in the field. 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] Various conditions in the field can have several adverse effects on harvesting operations. Therefore, when encountering such conditions during harvesting, the operator may try to modify the controls of the harvester.

[0004] The above discussion is provided only as general background information 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 information maps are obtained through agricultural machinery. These maps map one or more agricultural characteristic values ​​to different geographical locations within the field. As the agricultural machinery moves across the field, field sensors on the machinery detect the agricultural characteristics. A prediction map generator generates prediction maps of the predicted agricultural characteristics at different locations within the field based on the relationships between the values ​​in the one or more information maps and the agricultural characteristics sensed by the field sensors. These prediction maps can be output and used for automated machine control.

[0006] The present invention is provided to introduce selected concepts in a simplified form, which are further described in the detailed embodiments below. The present invention 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 pointed out 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 This is a block diagram showing some parts of an agricultural harvester in more detail, based on some examples of this disclosure.

[0009] Figures 3A to 3B A flowchart illustrating an example of the operation of an agricultural harvester when generating a diagram is shown.

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

[0011] Figure 5 This is a flowchart illustrating an operational example of an agricultural harvester receiving a map, detecting field characteristics, and generating a functional predictive map for display and / or use in controlling the agricultural harvester during harvesting operations.

[0012] Figure 6 This is a block diagram illustrating an example of a prediction model generator and a prediction graph generator.

[0013] Figure 7 The flowchart illustrates an example of how an agricultural harvester receives prior information maps and detects field sensor inputs when generating a functional prediction map.

[0014] Figure 8 This is a block diagram illustrating an example of (one or more) field sensors.

[0015] Figure 9 This is a block diagram illustrating an example of a control area generator.

[0016] Figure 10 It is a diagram. Figure 9 The flowchart shows an example of the operation of the control area generator.

[0017] Figure 11 This is a flowchart illustrating an example of how a control system operates when selecting a target setpoint to control an agricultural harvester.

[0018] Figure 12 This is a block diagram illustrating an example of an operator interface controller.

[0019] Figure 13 This is a flowchart illustrating an example of an operator interface controller.

[0020] Figure 14 This is an illustrative diagram showing an example of an operator interface display.

[0021] Figure 15 This is a block diagram illustrating an example of an agricultural harvester communicating with a remote server environment.

[0022] Figures 16 to 18 An example of a mobile device that can be used in agricultural harvesters is shown.

[0023] Figure 19 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 aforementioned figures. Detailed Implementation

[0024] To facilitate understanding of the principles of this disclosure, reference will now be made to the examples shown in the accompanying drawings, and they will be described using specific language. However, it will be understood that this is not intended to limit the scope of this disclosure. Any changes and further modifications to the described apparatus, systems, and methods, as well as any further applications of the principles of this disclosure, are fully contemplated, as would normally occur to those skilled in the art to which this disclosure pertains. Specifically, it is fully contemplated that features, components, steps, or combinations thereof described with respect to one example may be combined with features, components, steps, or combinations thereof described with respect to other examples of this disclosure.

[0025] This specification relates to generating functional predictive maps by combining data provided by the diagram with field data acquired concurrently with agricultural operations, and more specifically, generating functional predictive ear size maps. In some examples, functional predictive ear size maps can be used to control agricultural machinery (e.g., agricultural harvesters). The performance of agricultural harvesters may deteriorate when they engage in areas where ear size varies, unless machine settings are also changed. For example, if the cover plates on the harvester's header are not properly spaced, an ear or part of an ear may travel through the gaps defined by the spacing of the cover plates, resulting in grain loss due to contact with straw rollers positioned beneath the cover plates.

[0026] The vegetation index map graphically maps vegetation index values ​​(which can indicate vegetation growth) across different geographic locations in the field of interest. An example of a vegetation index includes the normalized difference vegetation index (NDVI). Many other vegetation indices exist, and all of them are within the scope of this disclosure. In some examples, vegetation indices can be derived from sensor readings of one or more electromagnetic radiation bands reflected by the plants. Without limitation, these electromagnetic radiation bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

[0027] Vegetation index maps can be used to identify the presence and location of vegetation. In some examples, vegetation index maps enable the identification and geolocation 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 midway through the growing season, it can indicate the developmental progress of crop plants. For example, a vegetation index map can indicate whether plants are underdeveloped, whether sufficient canopy has been established, or other plant attributes that indicate plant development.

[0028] Historical yield maps graphically map yield values ​​across different geographic locations in one or more fields of interest. These historical yield maps are collected from past harvesting operations of (one or more) fields. Yield maps can display yield in units of yield values. An example of a unit of yield value is dry bushels per acre. In some examples, historical yield maps can be derived from sensor readings of one or more yield sensors. Without limitation, these yield sensors can include gamma-ray attenuation sensors, impact plate sensors, load cells, cameras or other optical sensors, and ultrasonic sensors, etc.

[0029] Seeding maps graphically map the seeding characteristics across different geographic locations in a field of interest. These seeding maps are typically collected based on past seeding operations in the field. In some examples, seeding maps can be derived from control signals used by the seeder when planting seeds or from sensors on the seeder (e.g., sensors confirming whether seeds are delivered to furrows created by the seeder). The seeder may include geographic location sensors and topographic sensors that geolocate the location where seeds are planted and generate topographic information about the field. Information generated during previous seeding operations can be used to determine a variety of seeding characteristics, such as location (e.g., the geographic location of the planted seeds in the field), spacing (e.g., the spacing between two individual seeds, the spacing between seed rows, or both), population (which can be derived from spacing characteristics), orientation (e.g., seed orientation in furrows and orientation of seed rows), depth (e.g., seed depth or furrow depth), size (e.g., seed size), or genotype (e.g., seed species, seed hybrid, seed cultivar, etc.). Many other seeding characteristics can also be determined.

[0030] Alternatively or in addition to data from previous or a priori operations, a variety of different sowing characteristics on the sowing map can also be generated based on data from third parties (e.g., third-party seed suppliers providing seeds for seed planting operations). These third parties can provide a variety of different data indicating a variety of different seed characteristics, such as size data (e.g., seed size) or genotype data (e.g., seed species, seed hybrid, or seed cultivar). Furthermore, seed suppliers can provide a variety of different data related to specific plant characteristics of plants obtained from each different seed genotype. For example, data on plant growth (e.g., straw diameter, ear size, plant height, plant weight, etc.), plant response to weather conditions, plant response to applied substances (e.g., herbicides, fungicides, pesticides, insect repellents, fertilizers, etc.), plant response to pests, fungi, weeds, diseases, etc., and any number of other plant characteristics.

[0031] In some examples, a seeding map can be derived from sensor readings of one or more electromagnetic radiation bands reflected by the seeds. Without limitation, these electromagnetic radiation bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

[0032] This discussion pertains to a system that receives graphs such as prior information maps, graphs generated based on prior or previous operations, or predictive graphs (e.g., yield prediction maps). The system also uses field sensors to detect variables indicating one or more characteristics (e.g., agricultural characteristics). Agricultural characteristics are any characteristics that can affect agricultural operations (e.g., harvesting operations). In one example, one or more field sensors detect one or more variables indicating the ear size of vegetation, such as diameter or another dimension representing the cross-sectional size of the ear (collectively referred to herein as "diameter"), the length of the crop ear, or its weight; for example, one or more ear size sensors sensing the diameter, length, or weight of a maize ear. However, it should be noted that field sensors can detect variables indicating any number of other agricultural characteristics and are not limited to those described herein. The system generates a model that models the relationship between values ​​on the received graph and output values ​​from the field sensors. This model is used to generate a functional predictive graph based on values ​​from the received graph at different areas of the field, which predicts, for example, ear size, agricultural characteristics, or operator command inputs at different areas of the field. Functional predictive maps generated during harvesting operations can be presented to the operator or other users and / or used to automatically control the agricultural harvester during harvesting operations. The functional predictive maps can be used to control one or more controllable subsystems on the agricultural harvester. For example, a cover plate position controller generates control signals to control the machine actuator subsystem to adjust the position or spacing of the cover plates on the agricultural harvester.

[0033] Cover plates (also known as harvester plates) are included on the row units of the header (e.g., corn header) of an agricultural harvester. Typically, each row unit includes left and right cover plates. Each cover plate has an inner edge, and the inner edges of the left and right cover plates are spaced apart. The spacing between the left and right cover plates defines a gap for receiving vegetation (e.g., corn plants). This gap may be tapered, for example, tapering from rear (near the rear of the harvester) to front (where the stalks enter), such that the spacing between the front portions of the cover plates is narrower than the spacing at the rear portions. As the harvester travels through the field, as the row units move along aligned rows of corn plants, the gap defined by the spacing of the cover plates receives the corn stalks of that aligned row of corn plants. As the row unit moves along the row, corn stalks are drawn through the channel with the assistance of a collecting chain (usually located above the cover) or a stalk roller (sometimes called a snapping roller) (usually located below the cover), or both of which are located on the row unit. This causes the corn ears carried by the stalks to strike the cover and separate from the stalks. The separated corn ears are then further conveyed through an agricultural harvester, while the chopped stalk material is left on the field, either remaining on the field or later being gathered, such as as part of a stalk gathering process.

[0034] Proper settings for the mandrels on agricultural harvesters (such as position and spacing) are important for reducing losses (e.g., ear base stripping or ear jerking as the ear travels through gaps and contacts the stalk rollers) and for minimizing the introduction of non-grain materials (MOGs). In field conditions with highly variable ear sizes, the position and spacing of the mandrels can have a significant performance impact. For example, if the spacing between the mandrels is too wide, stripping (i.e., the stripping or removal of kernels from the cob when the ear base is allowed to contact the picking rollers) can occur, leading to grain loss at the header because the stripped kernels remain on the field. If the spacing between the mandrels is too narrow, premature stalk breakage can occur, and the harvester may introduce excessive MOGs along with the ear, potentially overloading the separator and making it more difficult to separate the grain from the MOGs on the screen, resulting in grain loss from the rear of the harvester as the residue is discharged. As harvesting speeds increase and header sizes grow larger, failure to properly and timely adjust the position and spacing of the cover plates can adversely affect the performance of agricultural harvesters.

[0035] 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 will be understood that this specification also applies 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. In addition, this disclosure relates to other types of operating machines, such as agricultural seeders and sprayers to which predictive mapping can be applied, construction equipment, forestry equipment, and turf 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.

[0036] like Figure 1 As shown, the agricultural harvester 100 exemplarily 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 feeder housing 106, a feed accelerator 108, and a thresher generally indicated by 110. The feeder housing 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. Therefore, the vertical position (cutting height) of the cutting table 102 above the ground 111 (where the cutting table 102 travels) can be controlled by actuating the actuator 107. Although Figure 1 As not shown, the agricultural harvester 100 may further include actuators operated to apply one or more of a tilt angle, a tumble angle, or both to the header 102 or a portion 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 further away from the ground. Tumble refers to the orientation of the header 102 about the longitudinal axis of the agricultural harvester 100.

[0037] The threshing machine 110 exemplarily includes a threshing rotor 112 and a set of concave plates 114. Additionally, the agricultural harvester 100 includes a separator 116. The agricultural harvester 100 also includes a cleaning subsystem or cleaning chamber 118 (collectively referred to as cleaning subsystem 118), which includes a cleaning fan 120, a chaff screen 122, and a screen 124. The material handling subsystem 125 also includes a discharge agitator 126, a waste lifter 128, a clean grain lifter 130, and an unloading screw conveyor 134 and a nozzle 136. The clean grain lifter moves clean grain into a clean grain bin 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, which includes an engine driving a ground engagement assembly 144 (e.g., wheels or tracks). In some examples, the combine harvester within the scope of this disclosure may have more than one of any of the above subsystems. In some examples, the agricultural harvester 100 may have Figure 1 The left and right grain cleaning subsystems and separators are not shown in the diagram.

[0038] In operation, as an overview, the agricultural harvester 100 exemplarily moves across the field in the direction indicated by arrow 147. As the agricultural 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 agricultural harvester 100 can be a local human operator, a remote human operator, or an automated system. Operator commands are commands from the operator. The operator of the agricultural harvester 100 can determine one or more of the header 102's height setting, tilt angle setting, or tumble angle setting. For example, the operator inputs one or more settings to the control system (described in more detail below) that controls the actuator 107. The control system can also receive settings from the operator for establishing the tilt angle and tumble angle of the header 102, and implement the input settings by controlling the associated actuators (not shown) to change the tilt angle and tumble angle of the header 102. Actuator 107 maintains the header 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 setting, tumble setting, and tilt setting can be implemented independently of the others. The control system responds to header errors (e.g., the difference between the height setting and the measured height of the header 104 above ground 111, and in some cases, tilt and tumble angle errors) with a responsiveness determined based on a selected sensitivity level. If the sensitivity level is set at a higher level, the control system responds to smaller header position errors and attempts to reduce the detected error faster than when the sensitivity level is lower.

[0039] 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 is conveyed in the feeder housing 106 towards the feed accelerator 108, which accelerates the crop material into the thresher 110. The crop is threshed by a rotor 112 that rotates the crop material against a concave plate 114. In a separator 116, a separator rotor moves the threshed crop, while a discharge agitator 126 moves a portion of the residue toward the residue subsystem 138. The portion of residue conveyed to the residue subsystem 138 is shredded by a residue shredder 140 and spread across the field by a spreader 142. In other configurations, the residue is discharged from the agricultural harvester 100 in piles. In other examples, the residue subsystem 138 may include a seed ejector (not shown), such as a seed bagger or other seed collector, or a seed shredder or other seed crusher.

[0040] The grain falls into the grain cleaning subsystem 118. A husk sieve 122 separates larger pieces of grain, while a screen 124 separates smaller pieces 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 then moves the clean grain upwards, causing it to settle in a clean grain bin 132. Airflow generated by a cleaning fan 120 removes residue from the grain cleaning subsystem 118. The cleaning fan 120 directs air upwards along an airflow path through the screen and husk sieve. The airflow then transports the residue backwards within the agricultural harvester 100 toward the residue handling subsystem 138.

[0041] The waste elevator 128 returns the waste to the threshing machine 110, where it is re-threshed. Alternatively, the waste may also be conveyed by the waste elevator or another conveying device to a separate re-threshing mechanism, where it is also re-threshed.

[0042] Figure 1 It is also shown that, 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 forward-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 grain cleaning subsystem 118.

[0043] 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 (e.g., 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, LoRAN (Local Remote Navigation System), or a variety of other systems or sensors that provide an indication of travel speed.

[0044] Loss sensor 152 exemplarily provides an output signal indicating the amount of grain loss occurring on both the right and left sides of the grain 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 the grain cleaning subsystem 118. The impact sensors on the right and left sides of the grain cleaning subsystem 118 can provide separate signals or combined or aggregated signals. In some examples, instead of providing separate sensors for each grain cleaning subsystem 118, sensor 152 may include a single sensor.

[0045] Separator loss sensor 148 provides indication of the left and right separators ( Figure 1 (Not shown separately) The separator loss sensor 148 can be associated with the left and right separators and can provide individual grain loss signals or combined or aggregated signals. In some cases, various types of sensors may also be used to sense grain loss in the separator.

[0046] 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 the oscillation 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, pile it, etc.; a cleaning chamber fan speed sensor that senses the speed of the cleaning fan 120; a concave plate gap sensor that senses the gap between the rotor 112 and the concave plate 114; and a threshing rotor speed sensor that senses the rotation speed of the rotor 112. The harvester 100 includes: a chaff screen gap sensor that senses the opening size in the chaff screen 122; a screen gap sensor that senses the opening size in the screen 124; a material other than grain (MOG) moisture sensor that senses the moisture level of the MOG passing through the harvester 100; one or more machine setting sensors configured to sense various configurable settings of the harvester 100; a machine orientation sensor that senses the orientation of the harvester 100; and a crop property sensor that senses various types of crop properties, such as crop type, crop moisture, and other crop properties. While the harvester 100 is processing crop material, the crop property sensor can also be configured to sense the characteristics of the cut crop material. 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 feed rate as the grain passes through the feeder housing 106, the clean grain elevator 130, or elsewhere in the harvester 100. The crop property sensor can also sense the feed rate of biomass through the feeder housing 106, separator 116, or elsewhere in the harvester 100. The crop property sensor can also sense the feed rate as the mass flow rate of grain through the elevator 130 or other parts of the harvester 100, or provide additional output signals indicating other sensed variables. The crop property sensor may include one or more yield sensors that sense the yield of the crop being harvested by the harvester.

[0047] One or more yield sensors may include grain flow sensors that detect the flow rate of crop (e.g., grain) in the material handling subsystem 125 or other parts of the agricultural harvester 100. For example, the yield sensor may include a gamma-ray attenuation sensor that measures the flow rate of harvested grain. In another example, the yield sensor includes an impact plate sensor that detects the impact of grain onto a sensing plate or surface to measure the mass flow rate of the harvested grain. In another example, the yield sensor includes one or more load cells that measure or detect the load or mass of the harvested grain. For example, one or more load cells may be located at the bottom of the grain bin 132, where changes in the weight or mass of the grain within the grain bin 132 during a measurement interval indicate the total yield during that measurement interval. The measurement interval may be increased for averaging or decreased for more instantaneous measurements. In another example, the yield sensor includes a camera or optical sensing device that detects the size or shape of the harvested grain aggregates, such as the shape or height of the grain pile in grain bin 132. Changes in the shape or height of the grain pile during a measurement interval represent the total yield during that interval. In other examples, other yield sensing techniques are employed. For example, in one example, the yield sensor includes two or more of the sensors described above, and the yield for the measurement interval is determined by signals output from each of several different types of sensors. For example, the yield is determined based on signals from a gamma-ray attenuation sensor, an impact plate sensor, a weighing sensor within grain bin 132, and an optical sensor along grain bin 132.

[0048] The crop property sensor may also include one or more ear specification sensors that sense the specifications of the ears of vegetation (e.g., ears of corn in a field), such as diameter, length, or weight.

[0049] The ear size sensor can be a sensor configured to sense the impact of the ear on a cover plate or the result of the impact (e.g., displacement of one or more cover plates). The ear size sensor may include accelerometers, strain gauge sensors, and any number of other sensors configured to detect the impact between the ear and the cover plate. In other examples, one or more ear size sensors may be optical sensors, such as cameras or other optical sensing devices (e.g., radar, lidar, sonar, etc.), which capture images of the vegetation around the combine harvester. The images containing the ear indication can be processed using any of a variety of image processing techniques to derive the ear size of the vegetation around the combine harvester. One or more of these and many other different ear size sensors can be used to provide a field indication of the ear size in the field where the combine harvester 100 is operating. It should be understood that these are merely some examples of ear size sensors, and those skilled in the art will appreciate that many other different ear size sensors can be used without departing from the spirit and scope of this disclosure. In some examples, the agricultural harvester may have one or more ear specification sensors, such as an ear specification sensor for each row unit on the header 102 of the agricultural harvester 100. In some examples, the agricultural harvester may have one or more ear specification sensors of different types.

[0050] Before describing how the agricultural harvester 100 generates a functional prediction map and uses that map for presentation or control, a brief description of some items on the agricultural harvester 100 and their operation will be provided first. Figure 2 , Figure 3A and Figure 3BThe diagram describes receiving a general type of prior information map and combining information from the prior information map with georegistered sensor signals generated by field sensors, where the sensor signals indicate characteristics of the field, such as the characteristics of crops or weeds present in the field. Field characteristics may include (but are not limited to): field properties such as slope, weed density, weed type, soil moisture, and surface quality; crop properties such as crop height, crop moisture, crop density, ear size, and crop condition; grain properties such as grain moisture, grain size, and grain test weight; and machine performance properties such as loss level, working quality, fuel consumption, and power utilization. Relationships between characteristic values ​​obtained from field sensor signals and prior information map values ​​are identified, and these relationships are used to generate new functional prediction maps. The functional prediction maps predict values ​​at different geographic locations in the field, and one or more of those values ​​can be used to control machines, such as one or more subsystems for controlling an agricultural harvester. In some cases, the functional prediction maps may be presented to users, such as operators of agricultural machinery (e.g., agricultural harvesters). Functional prediction maps can be presented to users visually (e.g., via a display), tactilely, or audibly. Users can interact with the functional prediction maps to perform editing operations and other user interface operations. In some cases, functional prediction maps can be used to control agricultural machinery (e.g., agricultural harvesters), presented to operators or other users, and presented to operators or users to facilitate operator or user interaction, among one or more of these methods.

[0051] In reference Figure 2 , Figure 3A and Figure 3B After describing the general method, refer to Figure 4 and Figure 5 More specific methods are described for generating functional predicted ear size diagrams that can be presented to an operator or user or used to control an agricultural harvester 100 or both. Similarly, although this discussion is directed toward agricultural harvesters (specifically, combine harvesters), the scope of this disclosure extends to other types of agricultural harvesters or other agricultural operating machines.

[0052] Figure 2 This is a block diagram showing some parts of an example agricultural harvester 100. Figure 2The agricultural harvester 100, as illustrated, exemplarily includes 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 characteristics of the field simultaneously with the harvesting operation. 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 various other agricultural harvester functions 220. For example, the field sensors 208 include onboard sensors 222, remote sensors 224, and other sensors 226 that sense field characteristics during agricultural operations. Figure 8 Some other examples of field sensors 208 are shown. Predictive model generator 210 exemplarily includes a prior information variable-to-field variable model generator 228, and predictive model generator 210 may include other items 230. Control system 214 includes a communication system controller 229, an operator interface controller 231, a setting controller 232, a path planning controller 234, a feed rate controller 236, a header and reel controller 238, a belt conveyor belt controller 240, a cover plate position controller 242, a residue system controller 244, a machine cleaning controller 245, a zone controller 247, and control system 214 may include other items 246. 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 controllable subsystem 216 may include various other subsystems 256.

[0053] Figure 2The agricultural harvester 100 is also shown to receive one or more prior information maps 258. As described below, for example, (one or more) prior information maps include, for example, vegetation index maps, yield maps, seeding maps, or maps from previous or prior operations in the field. However, (one or more) prior information maps 258 may also encompass other types of data obtained prior to the harvesting operation or maps from prior or previous operations, such as historical yield maps from the past few years containing background information related to historical yields. Background information may include (but is not limited to): one or more weather conditions throughout the growing season, the presence of pests, geographical location, soil type, irrigation, treatment applications, etc. Weather conditions may include (but are not limited to): precipitation throughout the season, the presence of hail that can damage crops, the presence of strong winds, temperature throughout the season, etc. Some examples of pests broadly include insects, fungi, weeds, bacteria, viruses, etc. Some examples of treatment applications include herbicides, insecticides, fungicides, fertilizers, mineral supplements, etc. Figure 2 The diagram also shows an operator 260 capable of operating an agricultural harvester 100. The operator 260 interacts with an operator interface mechanism 218. In some examples, the operator interface mechanism 218 may include a joystick, joystick, steering wheel, linkage, pedals, buttons, dials, keypad, user-actuable elements on a user interface display (e.g., icons, buttons, etc.), microphone and speaker (where speech recognition and speech synthesis are provided), and various other types of control devices. Where a touch-sensitive display system is provided, the operator 260 may interact with the operator interface mechanism 218 using touch gestures. The examples provided above are exemplary and not intended to limit the scope of this disclosure. Therefore, other types of operator interface mechanisms 218 may also be used and are within the scope of this disclosure.

[0054] Using communication system 206 or other methods, prior information map 258 can be downloaded to agricultural harvester 100 and stored in data storage device 202. In some examples, communication system 206 may be a cellular communication system, a system communicating via a wide area network or local area network, a system communicating via a near field communication network, or a communication system configured to communicate via any or a combination of various other networks. Communication system 206 may also include a system for facilitating the download or transfer of information to and from a Secure Digital (SD) card or a Universal Serial Bus (USB) card, or both.

[0055] The geolocation sensor 204 exemplarily senses or detects the geolocation or orientation of the agricultural harvester 100. The geolocation sensor 204 may include (but is not limited to) a Global Navigation Satellite System (GNSS) receiver that receives signals from a GNSS satellite transmitter. The geolocation sensor 204 may also include a Real-Time Kinematic (RTK) component configured to enhance the accuracy of position data derived from 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.

[0056] Field sensor 208 can be any of the sensors described above. Field sensor 208 includes onboard sensors 222 mounted on the agricultural harvester 100. For example, these sensors may include impact plate sensors, radiation attenuation sensors, or image sensors (e.g., clean grain cameras) inside the agricultural harvester 100. Field sensor 208 may also include remote field sensors 224 that capture field information. Field data includes data acquired from sensors on the agricultural harvester or, in the case of data detected during harvesting operations, data acquired by any sensor. Figure 8 Some examples of the field sensor 208 are shown.

[0057] After being retrieved by the agricultural harvester 100, the prior infographic selector 209 can filter or select one or more specific prior infographics 258 for use by the predictive model generator 210. In one example, the prior infographic selector 209 selects a infographic based on a comparison of background information in the prior infographic with current background information. For example, a historical yield infographic can be selected from a year in the past few years where the weather conditions throughout the growing season were similar to those of the current year. Alternatively, for example, when the background information is dissimilar, a historical yield infographic can be selected from a year in the past few years. For example, while the current year may be relatively "wet," a historical yield infographic from a previous year that was relatively "dry" could be selected. Useful historical relationships may still exist, but these relationships may be reversed. For example, an area with larger ear sizes in a relatively wet year may have an area with smaller ear sizes in a dry year. Current background information can include background information beyond the most recent background information. For example, current background information can include (but is not limited to): a set of information corresponding to the current growing season, a set of data corresponding to the winter preceding the current growing season, or a set of data corresponding to the past few years, etc.

[0058] Background information can also be used to correlate regions with similar background characteristics, regardless of whether the geographical location corresponds to the same location on prior information map 258. For example, historical yield values ​​from regions with similar soil types in other fields can be used as prior information map 258 to create a predicted ear size map. For example, background characteristic information associated with different locations can be applied to locations on prior information map 258 with similar characteristic information.

[0059] Predictive model generator 210 generates a model indicating the relationship between values ​​sensed by field sensors 208 and characteristics mapped to the field via prior information map 258. For example, if prior information map 258 maps vegetation index values ​​to different locations in the field, and field sensors 208 are sensing values ​​indicating ear size, then prior information variable-to-field variable model generator 228 generates a predictive ear size model that models the relationship between vegetation index values ​​and ear size values. Then, predictive map generator 212 uses the predictive ear size model generated by predictive model generator 210 to generate a functional predictive ear size map based on prior information map 258, which predicts the ear size values ​​expected to be sensed by field sensors 208 at different locations in the field. Alternatively, for example, if prior information map 258 maps historical yield values ​​to different locations in the field, and field sensors 208 are sensing values ​​indicating ear size, then prior information variable-to-field variable model generator 228 generates a predictive ear size model that models the relationship between historical yield values ​​(with or without contextual information) and field ear size values. Then, prediction map generator 212 uses the predictive ear size model generated by prediction model generator 210 to generate a functional predictive ear size map based on prior information map 258, which predicts the ear size values ​​expected to be sensed by field sensors 208 at different locations in the field.

[0060] 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 sensed by Field Sensor 208, but related to the data sensed by Field Sensor 208. 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 Prior Information Chart 258. In some cases, the data type in Functional Prediction Chart 263 may have a different unit than the data in Prior Information Chart 258. In some examples, the data type in Functional Prediction Chart 263 may be different from the data type in Prior Information Chart 258, but related to the data type in Prior Information Chart 258. For example, in some examples, the data type in Prior Information Chart 258 may indicate the type of data in Functional Prediction Chart 263. In some examples, the type of data in the functional prediction graph 263 is different from one or both of the field data type sensed by the field sensor 208 and the data type in the prior information graph 258. In some examples, the type of data in the functional prediction graph 263 is the same as one or both of the field data type sensed by the field sensor 208 and the data type in the prior information graph 258. In some examples, the type of data in the functional prediction graph 263 is the same as one of the field data type sensed by the field sensor 208 or the data type in the prior information graph 258, and different from the other.

[0061] Continuing with the vegetation index example above, prediction map generator 212 can use the vegetation index values ​​in prior information map 258 and the model generated by prediction model generator 210 to generate a functional prediction map 263 predicting ear size at different locations in the field. Prediction map generator 212 then outputs prediction map 264.

[0062] like Figure 2As shown, prediction map 264 is based on prior information values ​​from prior information map 258 at multiple locations across the field (or, even in different fields, at locations with similar background information) and uses a prediction model to predict the value of a characteristic at said location (this characteristic may be the same characteristic sensed by (one or more) field sensors 208) or the value of a characteristic related to the characteristic sensed by (one or more) field sensors 208. For example, if prediction model generator 210 has generated a prediction model indicating the relationship between vegetation index values ​​and ear size, then given vegetation index values ​​at different locations across the field, prediction map generator 212 generates prediction map 264 predicting the ear size values ​​at different locations across the field. Prediction map 264 is generated using the vegetation index values ​​at those locations obtained from prior information map 258 and the relationship between vegetation index values ​​and ear size obtained from the prediction model.

[0063] The following will describe some changes to the data types mapped in prior information graph 258, the data types sensed by field sensor 208, and the data types predicted in prediction graph 264.

[0064] In some examples, the data type in the prior infographic 258 differs from the data type sensed by the field sensor 208, while the data type in the prediction infographic 264 is the same as that sensed by the field sensor 208. For example, the prior infographic 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be ear size. Therefore, the prediction infographic 264 could be a predicted ear size map mapping the predicted ear size values ​​to different geographical locations in the field. In another example, the prior infographic 258 could be a sowing map, and the variable sensed by the field sensor 208 could be ear size. Thus, the prediction infographic 264 could be a predicted ear size map mapping the predicted ear size values ​​to different geographical locations in the field.

[0065] Furthermore, in some examples, the data type in the prior information map 258 differs from the data type sensed by the field sensor 208, and the data type in the prediction map 264 differs from both the data type in the prior information map 258 and the data type sensed by the field sensor 208. For example, the prior information map 258 may be a vegetation index map, and the variable sensed by the field sensor 208 may be an operator command input indicating the setting of the cover spacing. Therefore, the prediction map 264 may be a predicted ear size map that maps the predicted ear size value to different geographical locations in the field. In another example, the prior information map 258 may be a vegetation index map, and the variable sensed by the field sensor 208 may be ear size. Therefore, the prediction map 264 may be a predicted cover spacing setting map that maps the predicted cover spacing setting to different geographical locations in the field.

[0066] In some examples, the prior information map 258 is derived from previous passes through the field during prior or previous operations, and its data type differs from that sensed by the field sensor 208, while the data type in the prediction map 264 is the same as that sensed by the field sensor 208. For example, the prior information map 258 could be a seed population map generated during planting, and the variable sensed by the field sensor 208 could be ear size. Thus, the prediction map 264 could be a predicted ear size map that maps predicted ear size values ​​to different geographical locations in the field.

[0067] In some examples, the prior information map 258 is derived from previous passes through the field during a prior or previous operation, and the data type is the same as that sensed by the field sensor 208, and the data type in the prediction map 264 is also the same as that sensed by the field sensor 208. For example, the prior information map 258 could be a head size map generated in the previous year, and the variable sensed by the field sensor 208 could be head size. Thus, the prediction map 264 could be a predicted head size map that maps predicted head size values ​​to different geographic locations in the field. In this example, the prediction model generator 210 can use the relative head size differences from the georegistered prior information map 258 from the previous year to generate a prediction model that models the relationship between the relative head size differences on the prior information map 258 and the head size values ​​sensed by the field sensor 208 during the current harvest operation. The prediction map generator 212 then uses the prediction model to generate a predicted head size map.

[0068] In some examples, prediction map 264 may be provided to control zone generator 213. Control zone generator 213 groups adjacent portions of a region into one or more control zones based on data values ​​associated with adjacent portions of the prediction map 264. A control zone may include two or more consecutive portions of a region (e.g., a field), for which the control parameters corresponding to the control zone for controlling the controllable subsystem are constant. For example, changing the response time of the controllable subsystem 216 settings may not satisfactorily respond to changes in values ​​contained in a map such as prediction map 264. In this case, control zone generator 213 parses the map and identifies control zones with defined dimensions adapted to the response time of the controllable subsystem 216. In another example, control zones may be sized to reduce wear caused by excessive actuator movement due to continuous adjustment. In some examples, different groups of control zones may exist for individual controllable subsystems 216 or groups of controllable subsystems 216. Control zones may be added to prediction map 264 to obtain prediction control zone map 265. Therefore, except that the predicted control area map 265 includes control area information defining the control area, the predicted control area map 265 may be similar to the predicted map 264. Thus, as described herein, the functional predicted map 263 may or may not include a control area. Both predicted map 264 and predicted control area map 265 are functional predicted maps 263. In one example, the functional predicted map 263 does not include a control area (e.g., predicted map 264). In another example, the functional predicted map 263 does include a control area (e.g., 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.

[0069] It will 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 to a separate map displaying only the generated control regions. In some examples, the control regions can be used to control and / or calibrate the agricultural harvester 100. 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.

[0070] Predictive map 264 or predictive control area map 265, or both, are 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 transmit predictive map 264, predictive control area map 265, or both, to other remote systems.

[0071] The operator interface controller 231 is operable to generate control signals to control the operator interface mechanism 218. The operator interface controller 231 is also operable to present the prediction map 264 or the prediction control area map 265, or other information derived from or based on the prediction map 264, the prediction control area map 265, or both, to the operator 260. The operator 260 can be a local operator or a remote operator. As an example, the controller 231 generates control signals to control the display mechanism to display one or both of the prediction map 264 and the prediction control area map 265 to the operator 260. The controller 231 can generate an operator-actuable mechanism that is displayed and can be actuated by the operator to interact with the displayed map. The operator can edit the map by, for example, correcting the yield value displayed on the map based on the operator's observation. The setting controller 232 can generate control signals for various settings on the agricultural harvester 100 based on the prediction map 264, the prediction control area map 265, or both. For example, the setting 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, cover settings (e.g., cover spacing or cover position, or both), concave plate clearance, rotor settings, grain cleaning fan speed settings, header height, header function, reel speed, reel position, belt conveyor function (where the harvester 100 is coupled to a belt conveyor header), grain header function, internal distribution control, and other actuators 248 affecting other functions of the harvester 100. The path planning controller 234 exemplarily generates control signals to control the steering subsystem 252 to turn the harvester 100 according to a desired path. The path planning controller 234 can control the path planning system to generate a route for the harvester 100 and can control the 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 graph 264 or prediction control area graph 265, or both. For example, as the harvester 100 approaches a region where the yield is above a selected threshold, the feed rate controller 236 can reduce the speed of the harvester 100 to maintain a constant feed rate of grain or biomass through the machine. The header and reel controller 238 can generate control signals to control the header or reel or other header functions. The belt conveyor controller 240 can generate control signals based on prediction graph 264, prediction control area graph 265, or both, to control the belt conveyor belt or other belt conveyor functions.The cover position controller 242 can generate control signals based on prediction diagram 264 or prediction control area diagram 265, or both, to control the position of the cover included on the harvester. The residue system controller 244 can generate control signals based on prediction diagram 264 or prediction control area diagram 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, a specific type of machine cleaning operation 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 diagram 264 or prediction control area diagram 265, or both.

[0072] Figure 3A and Figure 3B A flowchart is shown, 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 prior information map 258.

[0073] At box 280, the agricultural harvester 100 receives a priori information map 258. Examples of receiving the priori information map 258 are discussed with reference to boxes 282, 284, and 286. As described above, the priori information map 258 maps the value of a variable corresponding to a first characteristic to different locations in the field, as indicated by box 282. For example, data may be collected based on aerial images or measured characteristics acquired in the previous year. This information may also be based on data detected in other ways (besides using aerial images). For example, in the previous year, the agricultural harvester 100 may have been equipped with sensors that detect and geolocate the characteristic as the harvester 100 travels through the field. This information may also be based on data detected in other ways (besides using aerial images). Box 284 indicates the data collected prior to the current harvesting operation, whether via aerial images or other sources. The agricultural harvester 100 can use the communication system 206 to download the prior information map 258 and store it in the data storage device 202. Alternatively, the communication system 206 can be used to load the prior information map 258 onto the agricultural harvester 100 in other ways, and... Figure 3A Box 286 in the flowchart indicates that prior information diagram 258 is loaded onto the agricultural harvester 100. In some examples, prior information diagram 258 may be received by communication system 206.

[0074] At box 287, the prior infographic selector 209 can select one or more infographics from a plurality of candidate prior infographics received in box 280. For example, historical yield maps over many years can be received as candidate prior infographics. Each of these infographics can contain background information, such as weather patterns over a period of time (e.g., one year), pest surges over a period of time (e.g., one year), soil type, etc. Background information can be used to select which historical yield maps should be selected. For example, weather conditions over a period of time (e.g., in the current year) or the current soil type of the field can be compared with the weather conditions and soil type in the background information of each candidate prior infographic. The result of this comparison can be used to select which historical yield map should be selected. For example, years with similar weather conditions can often produce similar yields or yield trends across the field. In some cases, years with opposite weather conditions may also help predict ear size based on historical yields. For example, an area with small ears in a dry year may have large ears in a wet year. The process of selecting one or more prior infographics by the prior infographic selector 209 can be manual, semi-automatic, or automatic. In some examples, during the harvesting operation, the prior infographic selector 209 can continuously or intermittently determine whether different prior infographics have a better relationship with the field sensor values. If a different prior infographic is more closely related to the field data, the prior infographic selector 209 can replace the currently selected prior infographic with the more relevant one.

[0075] At the start of the harvesting operation, field sensor 208 generates sensor signals indicating one or more field data values, which indicate plant characteristics, such as ear size, as indicated in box 288. Examples of field sensor 288 are discussed with reference to boxes 222, 290, and 226. As described above, field sensor 208 includes: an airborne sensor 222; a remote field sensor 224, such as a UAV-based sensor that flies once to collect field data (shown in box 290); or other types of field sensors specified by field sensor 226. Figure 8 Examples of field sensors 208 are shown. In some examples, position, heading, or speed data from geolocation sensor 204 is used to georeference data from airborne sensors.

[0076] Predictive model generator 210 controls prior information variables to pair with field variable model generator 228 to generate a model that models the relationship between the values ​​mapped in prior information graph 258 and the field values ​​sensed by field sensor 208, as indicated by box 292. The characteristics or data types represented by the values ​​mapped in prior information graph 258 and the field values ​​sensed by field sensor 208 can be the same or different characteristics or data types.

[0077] 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 prior information map 258 to generate a prediction map 264, which predicts the values ​​of different characteristics sensed by the field sensor 208 or related to the characteristics sensed by the field sensor 208 at different geographical locations in the harvesting field, as indicated by box 294.

[0078] It should be noted that in some examples, the prior information map 258 may include two or more different maps, or two or more different layers of a single map. 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. The individual maps in the two or more different maps, or the individual layers in the two or more different layers of a map, map different types of variables to geographical locations in the field. In this example, the predictive model generator 210 generates a predictive model that models the relationship between field data and the different variables mapped by the two or more different maps or two or more different layers. Similarly, the field sensors 208 may include two or more sensors, each sensing different types of variables. Therefore, the predictive model generator 210 generates a predictive model that models the relationship between the various types of variables mapped by the prior information map 258 and the various types of variables sensed by the field sensors 208. The prediction map generator 212 can use the prediction model and the various maps or layers in the prior information map 258 to generate a functional prediction map 263 that predicts the value of each sensed characteristic (or characteristic related to the sensed characteristic) sensed by the field sensor 208 at different locations in the harvesting field.

[0079] Prediction map generator 212 configures prediction map 264 such that prediction map 264 can be manipulated (or used) by control system 214. Prediction map generator 212 may provide prediction map 264 to control system 214 or control area generator 213 or both. Some examples of different ways in which prediction map 264 can be configured or output will be described with reference 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 different controllable subsystems of agricultural harvester 100, as indicated in box 296.

[0080] Control zone generator 213 can divide prediction map 264 into control zones based on values ​​on prediction map 264. Geographically contiguous values ​​within each other's thresholds can be grouped into a control zone. This threshold can be a default threshold, or it can be set based on operator input, input from the automation system, or other criteria. The size of the zones can be based on the responsiveness of control system 214, controllable subsystem 216, wear considerations, or other criteria, as indicated in box 295. Prediction map generator 212 configures prediction map 264 for presentation to an operator or other user. Control zone generator 213 can configure prediction control zone map 265 for presentation to an operator or other user. This is indicated in box 299. When presented to an operator or other user, the presentation of prediction map 264 or prediction control area map 265, or both, may include geographic location-related predicted values ​​on prediction map 264, geographic location-related control areas on prediction control area map 265, and one or more setpoints or control parameters used based on the predicted values ​​on prediction map 264 or the areas on prediction control area 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 areas on prediction control area map 265 conform to measurements that can be measured by sensors on the agricultural harvester 100 as the harvester 100 moves through the field. Furthermore, where information is presented to more than one location, an authentication / authorization system may be provided to enable authentication and authorization processes. For example, a hierarchy of individuals may exist who are authorized to view and modify the map and other presented information. As an example, the onboard display may display the map locally, approximately in real time, only on the machine, or the map may be generated at one or more remote locations. In some examples, each physical display device at each location can be associated with a person or user permission level. The user permission level can be used to determine which display markers are visible on the physical display device and which values ​​the corresponding person can change. As an example, the local operator of an agricultural harvester 100 may not be able to see the information corresponding to prediction map 264 or make any changes to the machine's operation. However, a supervisor at a remote location may be able to see prediction map 264 on the display but cannot make changes. A manager at a separate remote location may be able to see all elements on prediction map 264 and also change prediction map 264 used for machine control. This is an example of an achievable authorization hierarchy. Prediction map 264 or prediction control area map 265, or both, may also be configured in other ways, as indicated by box 297.

[0081] In box 298, the control system receives input from the geolocation sensor 204 and other field sensors 208. Box 300 indicates that the control system 214 receives input from the geolocation sensor 204 to identify 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.

[0082] At block 308, control system 214 generates control signals 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 signals to the controllable subsystem. It will be understood that the specific control signals generated and the specific controllable subsystem 216 controlled may vary based on one or more different things. For example, the generated control signals and the controllable subsystem 216 controlled may 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 signals, the controllable subsystem 216 controlled, and the control signals may be based on various delays in the crop flow through agricultural harvester 100 and the responsiveness of controllable subsystem 216.

[0083] As an example, the generated prediction map 264, in the form of a predicted ear specification map, can be used to control one or more controllable subsystems 216. For example, a functional predicted ear specification map may include ear specification values ​​at locations georegistered to harvested fields. The functional predicted ear specification map can be extracted and used to control the spacing or position of one or more sets of manhole covers on the header 102 of the combine harvester 100. The examples of manhole cover spacing or position using the functional predicted ear specification map above for prediction map 264 are provided as examples only. Therefore, values ​​obtained from the predicted ear specification map or other types of functional prediction maps 263 can be used to generate a variety of other control signals to control one or more controllable subsystems 216.

[0084] 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 continuously read.

[0085] In some examples, at box 316, the agricultural harvester 100 may also detect learning trigger criteria to perform machine learning on one or more of the following: the prediction graph 264, the prediction control area graph 265, the model generated by the prediction model generator 210, the area generated by the control area generator 213, one or more control algorithms implemented by the controller in the control system 214, and other triggered learning.

[0086] Learning triggering criteria can include any of a variety of different criteria. Some examples of triggering criteria detection are discussed with reference to boxes 318, 320, 321, 322, and 324. For example, in some examples, triggered learning may involve recreating the relationships used to generate a predictive model when a threshold amount of field sensor data is received from field sensor 208. In these examples, receiving a threshold amount of field sensor data from field sensor 208 triggers or causes 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, a new predictive map 264, predictive control area map 265, or both can be regenerated using the new predictive model. Box 318 indicates detecting a threshold amount of field sensor data used to trigger the creation of a new predictive model.

[0087] In other examples, the learning trigger criterion may be based on how much field sensor data from field sensor 208 has changed over time or compared to previous values. For example, if the change in the field sensor data (or the relationship between the field sensor data and the information in the prior information map 258) is within a selected range, less than a defined amount, or below a threshold, then the prediction model generator 210 does not generate a new prediction model. As a result, the prediction map generator 212 does not generate a new prediction map 264 and / or prediction control area map 265. However, for example, if the change in the field sensor data is outside the selected range, greater than a defined amount or threshold, or above a threshold, then the prediction model generator 210 uses all or part of the newly received field sensor data used by the 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 (e.g., the magnitude of the amount of data exceeding a selected range, or the magnitude of the change in the relationship between the field sensor data and the information in the prior information map 258) can be used as triggers to induce the generation of new prediction models and prediction maps. Continuing with the example described above, the threshold, range, and limited quantity can be set to default values, set by an operator or user through a user interface, set by an automation system, or set in other ways.

[0088] Other learning trigger criteria can also be used. For example, if the predictive model generator 210 switches to a different prior infographic (different from the initially selected prior infographic 258), switching to a different prior infographic can trigger the predictive model generator 210, the predictive map generator 212, the control area generator 213, the control system 214, or other items to relearn. In another example, the agricultural harvester 100 changing to a different terrain or a different control area can also be used as a learning trigger criterion.

[0089] In some cases, operator 260 may also edit prediction graph 264 or prediction control area graph 265, or both. This editing may change the values ​​on prediction graph 264 and / or change the size, shape, position, or presence of control areas on prediction control area graph 265. Box 321 shows that the edited information can be used as a learning trigger criterion.

[0090] In some cases, operator 260 may also observe that the automatic control of the controllable subsystem is not as desired by the operator. In these cases, operator 260 may provide manual adjustments to the controllable subsystem, reflecting the operator's expectation that the controllable subsystem will operate in a manner different from that commanded by control system 214. Therefore, operator 260's manual change of settings may cause one or more of the following to occur based on the adjustments made by operator 260 (as shown in box 322): causing predictive model generator 210 to relearn the model, causing predictive graph generator 212 to regenerate graph 264, causing control area generator 213 to regenerate one or more control areas on predictive control area graph 265, and causing control system 214 to relearn the control algorithm or perform machine learning on one or more components of controller components 232 to 246 in control system 214. Box 324 indicates the use of other triggered learning criteria.

[0091] In other examples, relearning can be performed periodically or intermittently based on, for example, selected time intervals (e.g., discrete or variable time intervals), as indicated by box 326.

[0092] As indicated in box 326, if relearning is triggered (whether based on a learning trigger criterion or on a past time interval), one or more of the predictive model generator 210, predictive graph generator 212, control area generator 213, and control system 214 perform machine learning to generate a new predictive model, a new predictive graph, a new control area, and a new control algorithm, respectively, based on the learning trigger criterion. Any additional data collected since the last learning operation is performed is used to generate the new predictive model, new predictive graph, and new control algorithm. The execution of relearning is indicated in box 328.

[0093] 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 may be stored locally on the data storage device 202 or sent to a remote system using the communication system 206 for subsequent use.

[0094] It should be noted that while some examples in this paper describe the predictive model generator 210 and the predictive graph generator 212 receiving prior information graphs when generating predictive models and functional predictive graphs, 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 functional predictive graphs generated during harvesting operations, when generating predictive models and functional predictive graphs, respectively.

[0095] Figure 4 yes Figure 1 A block diagram of a portion of an agricultural harvester 100. Specifically, among other things, Figure 4 Examples of the prediction model generator 210 and the prediction graph generator 212 are shown in more detail. Figure 4 The information flow between the various components shown herein is also illustrated. As shown, the prediction model generator 210 receives one or more of the following as graphs: a vegetation index map 332, a yield map 333 (such as a predicted yield map 335 or a historical yield map 337), a seeding map 399, or a priori operational map 400. In some examples, the model generator 210 may receive several different other maps 401. The predicted yield map 335 includes georegistered predicted yield values. The predicted yield map 335 can be generated using the process described in Figure 3, where the prior information map includes a vegetation index map or a historical yield map, and the field sensors include yield sensors. The predicted yield map 335 can also be generated in other ways. The historical yield map 337 includes historical yield values ​​indicating yield values ​​across the entire field during past harvests. The historical yield map 337 also includes background data indicating the context or conditions that may have affected yield values ​​in one or more past years. For example, background data may include soil type, altitude, slope, planting date, harvest date, fertilization, seed type (e.g., hybrids), measures of weed presence, measures of pest presence, and weather conditions (e.g., rainfall, snow cover, hail, wind, temperature, etc.). Historical yield chart 337 may also include other items.

[0096] In addition to receiving one or more maps, the predictive model generator 210 also receives a geographic location 334 or an indication of a geographic location from the geographic location sensor 204. The field sensor 208 exemplarily includes an ear-size sensor 336 and a processing system 338. In some examples, the ear-size sensor 336 may be located on an agricultural harvester 100. The processing system 338 processes the sensor data generated from the ear-size sensor 336. Figure 8 The image also shows some other examples of the field sensor 208.

[0097] In some examples, the ear size sensor 336 may be an optical sensor on an agricultural harvester 100. In some examples, the optical sensor may be a camera or other device performing optical sensing. The processing system 338 processes one or more images obtained via the ear size sensor 336 to generate processed image data identifying one or more characteristics of vegetation (e.g., crop plants) in the images. Vegetation characteristics detected by the processing system 338 may include size characteristics of plant ears (e.g., corn ears). For example, the processing system 338 may detect the diameter, length, or weight of ears included in the image.

[0098] The processing system 338 can also geolocate values ​​received from the field sensor 208. For example, the position of the harvester when the field sensor 208 sends a signal may not accurately represent the location of that value in the field. This is because a time interval may elapse between the moment the harvester makes initial contact with the feature and the moment the field sensor 208 senses the feature (or vice versa), especially in the case of a forward-looking ear-scale optical sensor, where a time interval may elapse between the moment the field sensor 208 senses the feature and the moment the harvester makes contact with the feature. Therefore, when georegistering the sensed data, the transient time between the moment the feature is encountered and the moment the field sensor 208 senses the feature (or vice versa) is taken into account. By doing so, the feature value can be accurately georegistered to its location in the field.

[0099] By way of illustration, in the context of yield values, as the cut crop travels along the header in a direction transverse to the direction of travel of the harvester, the yield value is typically geolocated to a V-shaped area behind the harvester as the harvester travels forward. Processing system 338, based on the travel time of the crop from different parts of the harvester (e.g., different lateral positions along the width of the harvester's header), allocates or distributes the total yield detected by the yield sensor during each time or measurement interval back to an earlier georegistered area. For example, processing system 338 allocates the total yield measured from a measurement interval or time back to georegistered areas traversed by the harvester's header during different measurement intervals or times. Processing system 338 distributes or assigns the total yield from a specific measurement interval or time to a previously traversed georegistered area, which is part of the V-shaped area.

[0100] In other examples, the ear size sensor 336 may rely on different types of radiation and how the radiation is reflected, absorbed, attenuated, or transmitted through the plant. The ear size sensor 336 may sense other electromagnetic properties of the grain and biomass, such as dielectric constant, as the material passes between two capacitive plates. The ear size sensor 336 may also rely on the mechanical properties of the vegetation, such as the signal generated when the ear strikes a piezoelectric element or when that impact is detected by a microphone or accelerometer. Other material properties and sensors may also be used. In some examples, raw or processed data from the ear size sensor 336 may be presented to an operator 260 via an operator interface mechanism 218. The operator 260 may be on the agricultural harvester 100 or at a remote location. The ear size sensor 336 may include any other examples described herein, as well as any other sensors configured to generate sensor signals indicating the size of an ear of vegetation (e.g., a corn ear). In some examples, data from multiple sensors may be used to determine ear size and size. To determine the specifications of a given ear of grain, one method can be selected from a set of methods based on whether the ear is hulled, partially hulled, unhulled, diseased, damaged, or some other limiting attribute.

[0101] This discussion is conducted with reference to an example in which the ear specification sensor 336 generates sensor signals indicating specification characteristics such as the diameter, length, or weight of a plant ear (e.g., the diameter, length, or weight of a corn ear). Figure 4 As shown, the prediction model generator 210 includes a vegetation index-ear size model generator 342, a yield-ear size model generator 344, and a sowing characteristic-ear size model generator 346. In other examples, model generator 210 may include ratios Figure 4The examples shown may include more, fewer, or different components. Therefore, in some examples, the predictive model generator 210 may also include other items 348, which may include other types of predictive model generators to generate other types of ear specification models. For example, the predictive model generator 210 may include a prior or a priori operational characteristic to ear specification model generator, such as historical ear specifications detected during a previous harvesting operation, and thus the predictive model generator 210 may include a historical ear specification to ear specification model generator that determines the relationship between historical ear specification values ​​and field-detected ear specification values.

[0102] The vegetation index-ear size model generator 342 determines the relationship between the field ear size data 340 at a geographic location corresponding to the geographic location where the field ear size data 340 is georeferenced, and the vegetation index value from the vegetation index map 332 corresponding to the same location in the field where the ear size data 340 is georeferenced. Based on this relationship established by the vegetation index-ear size model generator 342, the vegetation index-ear size model generator 342 generates a predicted ear size model. The prediction map generator 212 uses this ear size model to predict the ear size at the corresponding location in the field based on the georeferenced vegetation index values ​​at different locations in the field included in the vegetation index map 332.

[0103] The yield-to-ear specification model generator 344 determines the relationship between the field ear specification data 340 at a geographic location corresponding to the geographic location where the field ear specification data 340 is georeferenced, and the yield value from the yield map 333 corresponding to the same location in the field where the ear specification data 340 is georeferenced. Based on this relationship established by the yield-to-ear specification model generator 344, the yield-to-ear specification model generator 344 generates a predicted ear specification model. The prediction map generator 212 uses this ear specification model to predict the ear specification at the corresponding location in the field based on the georeferenced yield values ​​at different locations in the field included in the yield map 333.

[0104] The sowing characteristic to ear size model generator 346 determines the relationship between the field ear size data 340 at a geographic location corresponding to the geographic location where the field ear size data 340 is georeferenced, and the sowing characteristic value from the sowing map 339 corresponding to the same location in the field where the ear size data 340 is georeferenced. Based on this relationship established by the sowing characteristic to ear size model generator 346, the sowing characteristic to ear size model generator 346 generates a predicted ear size model. The prediction map generator 212 uses this ear size model to predict the ear size at the corresponding location in the field based on the georeferenced sowing characteristic values ​​at different locations in the field included in the sowing map 339.

[0105] In other examples, model generator 210 may include other model generators 348. Based on the relationships established by the other model generators, the model generators generate a predictive ear size model. The predictive map generator 212 uses this ear size model to predict the ear size at the same location in the field based on georegistered characteristic values ​​contained in the map at different locations in the field.

[0106] In view of the foregoing, the prediction model generator 210 is operable to generate multiple prediction ear size models, such as one or more of the prediction ear size models generated by model generators 342, 344, 346, and 348. In another example, two or more of the above-mentioned prediction ear size models can be combined into a single prediction ear size model, which predicts ear size based on vegetation index values, sowing characteristic values, prior operational characteristic values, or yield values, or combinations thereof, at different locations in the field. Any one or a combination of these ear size models is generated by... Figure 4 The ear specification model 350 is uniformly represented in the model.

[0107] The predicted ear size model 350 is provided to the prediction map generator 212. Figure 4 In one example, the prediction map generator 212 includes a spike size map generator 352. In other examples, the prediction map generator 212 may include additional or different map generators. Therefore, in some examples, the prediction map generator 212 may also include other items 358, which may include other types of prediction map generators to generate other types of prediction maps. The spike size map generator 352 receives a prediction spike size model 350 based on field data 340 to predict spike size, and one or more of a vegetation index map 332, a yield map 333, a seeding map 399, a priori operation map 400, or other maps 401.

[0108] The ear size map generator 352 can generate a functional predicted ear size map 360 based on vegetation index values, yield values, sowing characteristic values, prior operational characteristic values, or other characteristic values ​​at different locations in the field, as well as the predicted ear size model 350. This functional predicted ear size map 360 predicts the ear size values ​​at different locations in the field. The generated functional predicted ear size map 360 can be provided to the control area generator 213, the control system 214, or both, such as... Figure 2 As shown. Control zone generator 213 generates control zones and combines those control zones to provide a functional predicted ear specification diagram 360 with control zones. The functional predicted ear specification diagram 360 (with or without control zones) can be presented to operator 260 or another user, or provided to control system 214, which generates control signals based on the functional predicted ear specification diagram 360 (with or without control zones) to control one or more of the controllable subsystems 216.

[0109] Figure 5 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 ear size model 350 and a functional prediction ear size map 360. At box 362, the prediction model generator 210 and the prediction map generator 212 receive one or more maps, such as one or more vegetation index maps 332, one or more yield maps 333, one or more sowing maps 399, one or more previous operation maps 400, or one or more other maps 401, or any combination thereof. At box 364, field sensor signals are received from field sensor 208. As discussed above, field sensor 208 may include ear size sensor 336, which generates sensor signals indicating ear size characteristics (e.g., the diameter, length, or weight of maize ears in the field). In some examples, field sensor 208 may include a variety of other sensors, as indicated by box 370. Figure 8 Some other examples of other field sensors 208 are shown in the figure.

[0110] At box 372, the processing system 338 processes one or more received sensor signals received from the ear specification sensor 336 to generate ear specification values ​​that indicate the ear specification of vegetation on the field (e.g., the specification of a corn ear).

[0111] At box 382, ​​the prediction model generator 210 also acquires the geographic location corresponding to the sensor signal. For example, the prediction model generator 210 can acquire the geographic location from the geographic location sensor 204 and determine the accurate geographic location to which the ear size sensed in the field belongs based on machine latency (e.g., machine processing speed, sensor attributes, etc.) and machine speed. For example, the location of the agricultural harvester 100 when it captures the ear size sensor signal may not correspond to the accurate location of the sensed ear (or the plant with the sensed ear) in the field. Therefore, the location of the agricultural harvester 100 when it acquires the ear size sensor signal may not correspond to the location of that ear (or the plant with that ear).

[0112] At box 384, the prediction model generator 210 generates one or more prediction ear size models (e.g., ear size model 350) that model the relationship between at least one of a vegetation index value, a seeding characteristic value, a priori operational characteristic value, or a yield value obtained from a graph such as vegetation index graph 332, seeding graph 399, prior operational characteristic graph 400, or yield graph 333, and ear size values ​​detected by field sensor 208. The model generator 210 can generate the prediction ear size model based on the vegetation index value, seeding characteristic value, prior operational characteristic value, or yield value, as well as the detected ear size value indicated by sensor signals obtained from field sensor 208.

[0113] At box 386, a predicted ear size model (e.g., predicted ear size model 350) is provided to a prediction map generator 212, which generates a functional predicted ear size map based on a vegetation index map, a seeding map, a priori operation map, or a yield map, and the predicted ear size model 350. This functional predicted ear size map maps predicted ear size values ​​to different geographic locations in the field. For example, in some examples, the functional predicted ear size map 360 predicts ear size characteristics, such as diameter, length, or weight, or values ​​indicating ear size characteristics. In other examples, the functional predicted ear size map 360 predicts other items, as indicated by box 392. Furthermore, this functional predicted ear size map 360 can be generated during agricultural harvesting operations. Thus, the functional predicted ear size map 360 is generated as an agricultural harvester moves through the field where an agricultural harvesting operation is being performed.

[0114] At block 394, the prediction map generator 212 outputs a functional predicted ear specification map 360. At block 393, the prediction map generator 212 configures this functional predicted ear specification map 360 for use by the control system 214. At block 395, the prediction map generator 212 can also provide the functional predicted ear specification map 360 to the control area generator 213 for the generation and combination of control areas. At block 397, the prediction map generator 212 also configures the functional predicted ear specification map 360 in other ways. The functional predicted ear specification map 360 (with or without control areas) is provided to the control system 214. At block 396, the control system 214 generates control signals based on the functional predicted ear specification map 360 to control the controllable subsystem 216.

[0115] Control system 214 can generate control signals to control the header or (one or more) other machine actuators 248, for example, to control the position or spacing of the cover plates. Control system 214 can generate control signals to control the propulsion subsystem 250. Control system 214 can generate control signals to control the steering subsystem 252. Control system 214 can generate control signals to control the residue subsystem 138. Control system 214 can generate control signals to control the machine cleaning subsystem 254. Control system 214 can generate control signals to control the thresher 110. Control system 214 can generate control signals to control the material handling subsystem 125. Control system 214 can generate control signals to control the crop cleaning subsystem 118. Control system 214 can generate control signals to control the communication system 206. Control system 214 can generate control signals to control the operator interface mechanism 218. Control system 214 can generate control signals to control a variety of other controllable subsystems 256. In other examples, control system 214 may generate control signals to control the speed of threshing drum 112, to control the gap between the concave plates, or to adjust the power or energy output of some plant processing system (such as a collection chain or straw roller).

[0116] Figure 6 yes Figure 1 A block diagram of an example portion of the agricultural harvester 100 shown. Specifically, among other things, Figure 6Examples of a prediction model generator 210 and a prediction map generator 212 are shown. In the illustrated example, the prior information map 258 may be a vegetation index map 332, a sowing map 399, a yield map 333 (e.g., a historical yield map 337), or a prior operation map 400. The prior operation map 400 may include characteristic values ​​indicating characteristics at multiple locations in the field. These characteristic values ​​may be characteristic values ​​collected during a previous or prior operation (e.g., a previous or prior operation on the field by another agricultural operation machine (e.g., a sprayer)). The prior information map 258 may also include other prior information maps 401, such as a prior ear specification map generated or otherwise provided in various different ways. In one example, the prior ear specification map may be a historical ear specification map generated based on data collected during a previous harvest operation (e.g., a previous harvest operation in a previous harvest). Figure 6 It is also shown that the prediction model generator 210 and prediction map generator 212, in addition to receiving the prior information map 258, can also receive a functional predicted ear specification map 360 and a yield map 333 (e.g., a predicted yield map 335). The functional predicted ear specification map 360 and the predicted yield map 335 can be used similarly to the prior information map 258, because the model generator 210 models the relationship between the information provided by the functional predicted ear specification map 360 or the predicted yield map 335 and the characteristics sensed by the field sensor 208 to generate a prediction model. The map generator 212 can use the prediction model generated by the model generator 210 to generate a functional prediction map based on one or more values ​​in the functional predicted ear specification map 360 or the predicted yield map 335 at different locations in the field, and based on the prediction model, which predicts the characteristics or related characteristics sensed by the field sensor 208 at said different locations in the field. In some examples, yield map 333 is a priori information map, such as yield map 337, or a predicted yield map, such as predicted yield map 335. Prediction model generator 210 and prediction map generator 212 can also receive a variety of other maps 401, such as other predicted ear specification maps generated in a manner different from the functional predicted ear specification map 360.

[0117] In addition, Figure 6 In the example shown, the field sensor 208 may include one or more agricultural characteristic sensors 402, operator input sensors 404, and a processing system 406. The field sensor 208 may also include other sensors 408. Figure 8 Some other examples of field sensors 208 are shown. Agricultural characteristic sensor 402 may include one or more of the field sensors 208 described herein. Agricultural characteristic sensor 402 senses one or more variables indicating agricultural characteristics.

[0118] Operator input sensor 404 exemplarily senses a variety of different operator inputs. Inputs may be setting inputs or other control inputs, such as steering inputs and others, used to control settings on the harvester 100. Thus, when the operator of the harvester 100 (e.g., operator 260) changes settings or provides command input, for example through operator interface mechanism 218, such inputs are detected by operator input sensor 404, which provides a sensor signal indicating the sensed operator input. For the purposes of this disclosure, operator input may also be referred to as a characteristic, such as an agricultural characteristic, and therefore, operator input can be an agricultural characteristic sensed by field sensor 208. Processing system 406 may receive sensor signals from agricultural characteristic sensor 402 or operator input sensor 404, or both, and generate an output indicating the detected characteristic. For example, processing system 406 may receive sensor input from agricultural characteristic sensor 402 and generate an output indicating an agricultural characteristic. Processing system 406 may also receive input from operator input sensor 404 and generate an output indicating the sensed operator input.

[0119] Predictive model generator 210 may include ear specification to agricultural trait model generator 416, ear specification to command model 422, and other trait to command model generator 423. In other examples, predictive model generator 210 may include more, fewer, or other model generators 424, such as specific agricultural trait model generators. Furthermore, other trait to command model generator 423 may include, as other trait, vegetation index values ​​provided by vegetation index map 332, sowing trait values ​​provided by sowing map 399, prior operational trait values ​​provided by prior operational map 400, or yield values ​​provided by yield map 333. Predictive model generator 210 may receive geographic location 334 or geographic location indication from geographic location sensor 204 and generate predictive model 426, which models the relationship between information from one or more of the maps and one or more agricultural traits sensed by agricultural trait sensor 402 and / or one or more operator input commands sensed by operator input sensor 404. For example, the ear specification to agricultural characteristic generator 416 generates a model that models the relationship between ear specification values ​​(which may be on or indicated by one or more graphs) and agricultural characteristic values ​​sensed by agricultural characteristic sensor 402. The ear specification to command model generator 422 generates a model that models the relationship between ear specification values ​​(which may be on or indicated by one or more graphs) and operator input commands sensed by operator input sensor 404. The other characteristic to command model generator 423 generates a model that models the relationship between other characteristic values ​​(such as vegetation index values, prior operational characteristic values, sowing characteristic values, or yield values) and operator input commands sensed by operator input sensor (e.g., operator input commands instructing the spacing or position settings of one or more sets of manhole covers on an agricultural harvester).

[0120] The prediction model 426 generated by the prediction model generator 210 may include one or more prediction models that may be generated by the ear specification to agricultural trait model generator 416, the ear specification to command model generator 422, the other trait to command model generator 423, and other model generators that may be included as part of other items 424.

[0121] exist Figure 6In one example, the prediction map generator 212 includes a prediction agricultural characteristic map generator 428 and a prediction operator command map generator 432. In other examples, the prediction map generator 212 may include more, fewer, or other map generators 434. The prediction agricultural characteristic map generator 428 receives one or more of the maps and a prediction model 426 (e.g., a prediction model generated by the ear specification to agricultural characteristic model generator 416), which models the relationship between one or more values ​​provided by one or more of the maps and one or more agricultural characteristic values ​​sensed by the agricultural characteristic sensor 402. The prediction agricultural characteristic map generator 428 generates a functional prediction agricultural characteristic map 436 based on values ​​(e.g., ear specification values) contained in one or more of the maps corresponding to any given location in the field and based on the prediction model 426. This functional prediction agricultural characteristic map 436 predicts the agricultural characteristic at that location in the field.

[0122] The predictive operator command graph generator 432 receives one or more of the graphs and a predictive model 426 (e.g., a predictive model generated by the ear specification-to-command model generator 422) that models the relationship between one or more ear specification values ​​and one or more operator command inputs, or a predictive model 426 (e.g., a predictive model generated by the other trait-to-command model generator 423) that models the relationship between one or more other characteristics and one or more operator command inputs. The predictive operator command graph generator 432 generates a functional predictive operator command graph 440 based on one or more values ​​at different locations in the field in one or more of the graphs and based on the predictive model 426. This functional predictive operator command graph 440 predicts operator commands at the different locations in the field. For example, the predictive operator command map generator 432 generates a functional predictive operator command map 440 based on the ear size value, yield value, vegetation index value, sowing characteristic value, or prior operational characteristic value contained in the functional predictive ear size map 360, yield map 333, vegetation index map 332, sowing map 399, or prior operational characteristic map 400, which corresponds to any given location in the field. The functional predictive operator command map 440 predicts the operator command at said location.

[0123] The prediction graph generator 212 outputs one or more functional prediction graphs 436 or 440. Each of the functional prediction graphs 436 or 440 can be provided to the control area generator 213, the control system 214, or both, such as... Figure 2As shown, control zone generator 213 generates control zones and combines those control zones to provide a functional predicted agricultural characteristic diagram 436 with control zones and a functional predicted operator command diagram 440 with control zones. One or more of the functional predicted diagrams 436 or 440 (with or without control zones) can be provided to control system 214, which generates control signals based on one or more of the functional predicted diagrams 436 or 440 (with or without control zones) to control one or more controllable subsystems (e.g., controllable subsystem 216) of the agricultural harvester 100. One or more of the functional predicted diagrams 436 or 440 (with or without control zones) can be presented to operator 260 or another user.

[0124] Figure 7 A flowchart illustrating an example of the operation of the predictive model generator 210 and the predictive graph generator 212 in generating one or more predictive models 426 and one or more functional predictive graphs 436 or 440, respectively. At box 442, the predictive model generator 210 and the predictive graph generator 212 receive a graph. This graph can be in... Figure 6 One or more of the figures shown are included, such as vegetation index figure 332, yield figure 333, sowing figure 39, prior operation figure 400, functional prediction straw diameter figure 360, or other figures 401. At box 444, the prediction model generator 210 receives sensor signals containing sensor data from field sensors 208. The field sensors can be one or more of agricultural characteristic sensors 402, operator input sensors 404, or other sensors 408. Figure 8 Examples of field sensors 208 are shown. Box 446 indicates that sensor signals received by the prediction model generator 210 include data indicating the type of agricultural characteristics. Box 450 indicates that sensor signals received by the prediction model generator 210 can be sensor signals with data indicating the type of operator command input. The prediction model generator 210 may also receive other field sensor inputs, as indicated by box 452.

[0125] At box 454, processing system 406 processes data contained in one or more sensor signals received from one or more field sensors 208 to obtain processed data 409, such as... Figure 6As shown. Data contained in one or more sensor signals may be in its raw format, which is processed to receive processed data 409. For example, temperature sensor signals include resistance data, which can be processed into temperature data. In other examples, processing may include digitizing, encoding, formatting, scaling, filtering, or classifying the data. The processed data 409 may indicate one or more agricultural characteristics or operator input commands. The processed data 409 is provided to the predictive model generator 210.

[0126] Back Figure 7 At box 456, the prediction model generator 210 also receives a location 334 or a location indication from the location sensor 204, such as... Figure 6 As shown. Geographic location 334 can be associated with the geographic location of one or more sensed variables sensed by field sensor 208. For example, predictive model generator 210 can obtain geographic location 334 or an indication of geographic location from geographic location sensor 204, and determine the precise geographic location based on machine delay, machine speed, etc., from which processed data 409 is derived.

[0127] At box 458, prediction model generator 210 generates one or more prediction models 426, which model the relationship between the mapping values ​​in the graph received at box 442 and the characteristics represented in the processed data 409. For example, in some cases, the mapping values ​​in the received graph may be ear specification values; and prediction model generator 210 uses the mapping values ​​of the received graph and characteristics sensed by field sensor 208 (such as those represented in the processed data 490) or related characteristics (such as those related to the characteristics sensed by field sensor 208) to generate prediction models.

[0128] For example, at box 460, prediction model generator 210 can generate a prediction model 426 that models the relationship between ear specification values ​​obtained from one or more plots and agricultural characteristic data obtained from field sensors 208. In another example, at box 462, prediction model generator 210 can generate a prediction model 426 that models the relationship between ear specification values ​​obtained from one or more plots and operator command inputs obtained from field sensors 208. In another example, at box 463, prediction model generator 210 can generate a prediction model 426 that models the relationship between other characteristic values ​​obtained from one or more plots and operator command inputs obtained from field sensors 208. Model generator 210 can generate a variety of other prediction models that model the relationship between other characteristic values ​​obtained from one or more plots and data from one or more field sensors 208.

[0129] One or more prediction models 426 are provided to the prediction map generator 212. At box 466, the prediction map generator 212 generates one or more functional prediction maps. The functional prediction maps can be one or more functional prediction agricultural characteristic maps 436 or one or more functional prediction operator command maps 440, or any combination of these maps. The functional prediction agricultural characteristic map 436 predicts agricultural characteristics at different locations in the field. The functional prediction operator command map 440 predicts desired or possible operator command inputs at different locations in the field. Furthermore, one or more functional prediction maps 436 and 440 can be generated during agricultural operations. Therefore, when the agricultural harvester 100 moves across the field to perform agricultural operations, one or more prediction maps 436 and 440 are generated during the performance of those operations.

[0130] At box 468, the prediction graph generator 212 outputs one or more functional prediction graphs 436 and 440. At box 470, the prediction graph generator 212 can configure one or more of these graphs to be presented to operator 260 or other users and for possible interaction with operator 260 or other users. At box 472, the prediction graph generator 212 can configure one or more of these graphs for use by the control system 214. At box 474, the prediction graph generator 212 can provide one or more prediction graphs 436 and 440 to the control area generator 213 for the generation and combination of control areas. At box 476, the prediction graph generator 212 otherwise configures one or more prediction graphs 436 and 440. The one or more functional prediction graphs 436 and 440 can be presented to operator 260 or another user, or can also be provided to the control system 214.

[0131] At box 478, the control system 214 then generates control signals based on one or more functional prediction maps 436 or 440 (or functional prediction maps 436 or 440 with control areas) and input from the geolocation sensor 204 to control a controllable subsystem (e.g., controllable subsystem 216) of the agricultural harvester 100. For example, when a functional prediction agricultural characteristic map 436 is provided to the control system 214, in response, one or more controllers generate control signals based on predicted agricultural characteristic values ​​in the functional prediction agricultural characteristic map 436 or the functional prediction agricultural characteristic map 436 containing control areas to control one or more controllable subsystems 216 in order to control the operation of the agricultural harvester 100. In another example, when the functional predictive command diagram 440 is provided to the control system 214, in response, one or more controllers generate control signals based on the predicted operator command values ​​in the functional predictive command diagram 440 or the functional predictive command diagram 440 containing the control area to control one or more controllable subsystems 216 to control the operation of the agricultural harvester 100. This is indicated by box 480.

[0132] Box 484 illustrates an example in which the control system 214 receives a functional predictive operator command diagram 440 or a functional predictive operator command diagram 440 with an added control area. In response, the setting controller 232 generates control signals to control other machine settings or machine functions based on the predicted operator command input in the functional predictive operator command diagram 440 or the functional predictive operator command diagram 440 with an added control area. Box 485 illustrates that control signals for controlling the operation of the agricultural harvester 100 can also be generated in other ways, for example, based on a combination of functional predictive diagrams 436 or 440. For example, based on functional prediction diagrams 436 or 440 (with or without a control area) or both, one or more controllers generate control signals based on predicted agricultural characteristic values ​​in functional prediction agricultural characteristic diagram 436 or functional prediction agricultural characteristic diagram 436 including a control area, or operator command values ​​in functional prediction operator command diagram 440 or functional prediction operator command diagram 440 including a control area, to control one or more controllable subsystems 216 in order to control the operation of the agricultural harvester 100.

[0133] Control system 214 can generate control signals to control the header or (one or more) other machine actuators 248 to control the position of the cover plates or the spacing between the cover plates. Control system 214 can generate control signals to control the propulsion subsystem 250. Control system 214 can generate control signals to control the steering subsystem 252. Control system 214 can generate control signals to control the residue subsystem 138. Control system 214 can generate control signals to control the machine cleaning subsystem 254. Control system 214 can generate control signals to control the thresher 110. Control system 214 can generate control signals to control the material handling subsystem 125. Control system 214 can generate control signals to control the crop cleaning subsystem 118. Control system 214 can generate control signals to control the communication system 206. Control system 214 can generate control signals to control the operator interface mechanism 218. Control system 214 can generate control signals to control a variety of other controllable subsystems 256. In other examples, control system 214 may generate control signals to control the speed of threshing drum 112, to control the gap between the concave plates, or to adjust the power or energy output of some plant processing system (such as a collection chain or straw roller).

[0134] Figure 8 A block diagram illustrating an example of a real-time (field) sensor 208 is shown. The field sensor 208 can sense any of a variety of agricultural characteristics. Figure 8 Some of the sensors or different combinations of sensors shown may simultaneously have sensor 402 and processing system 406, while other sensors may be used as references above. Figure 6 and Figure 7 The described sensor 402, in Figure 6 and Figure 7 The processing system 406 is either separate or independent. Figure 8 Some of the possible field sensors 208 shown are illustrated and described relative to the previous figures and are similarly numbered. Figure 8The field sensor 208 is shown to include an operator input sensor 980, a machine sensor 982, a harvested material property sensor 984, a field and soil property sensor 985, and an environmental property sensor 987, and may also include a variety of other sensors 226. The operator input sensor 980 may be a sensor that senses operator input via an operator interface mechanism 218. Therefore, the operator input sensor 980 can sense user movement via linkages, joysticks, steering wheels, buttons, dials, pedals, or other user input devices. The operator input sensor 980 can also sense user interaction with other operator input mechanisms, such as interaction with a touchscreen, with a microphone utilizing voice recognition, or with any of the various other operator input mechanisms.

[0135] Machine sensor 982 can sense various characteristics of the agricultural harvester 100. For example, as described above, machine sensor 982 may include machine speed sensor 146, separator loss sensor 148, clean grain camera 150, forward-view image capture mechanism 151, loss sensor 152, or geolocation sensor 204, examples of which have been described above. Machine sensor 982 may also include machine setting sensor 991 for sensing machine settings. (See above references) Figure 1Examples of machine settings are described. A front-end device (e.g., header) position sensor 993 can sense the position of the header 102, reel 164, cutter 104, or other front-end devices, such as their position relative to the frame of the harvester 100 or relative to the field surface. For example, sensor 993 can sense the height of the header 102 above the ground. Machine sensor 982 may also include a front-end device (e.g., header) orientation sensor 995. Sensor 995 can sense the orientation of the header 102 (such as pitch or roll), such as its orientation relative to the harvester 100 or relative to the ground. Machine sensor 982 may include a stability sensor 997. Stability sensor 997 senses vibrational or bouncing movements (and amplitude) of the harvester 100. Machine sensor 982 may also include a residue setting sensor 999, configured to sense whether the harvester 100 is configured to shred residue, create a stockpile, or otherwise process residue. Machine sensor 982 may include a cleaning chamber fan speed sensor 951 that senses the speed of the cleaning fan 120. Machine sensor 982 may include a concave plate gap sensor 953 that senses the gap between the drum 112 and the concave plate 114 on the agricultural harvester 100. Machine sensor 982 may include a husk sieve gap sensor 955 that senses the size of the openings in the husk sieve 122. Machine sensor 982 may include a threshing rotor speed sensor 957 that senses the rotor speed of the drum 112. Machine sensor 982 may include a rotor force sensor 959 that senses the force (e.g., pressure, torque, etc.) used to drive the drum 112. Machine sensor 982 may include a screen gap sensor 961 that senses the size of the openings in the screen 124. Machine sensor 982 may include a MOG humidity sensor 963 that senses the humidity level of the MOG passing through the harvester 100. Machine sensor 982 may include a machine orientation sensor 965 that senses the orientation (such as pitch or roll) of the harvester 100 relative to the frame or relative to the field surface. Machine sensor 982 may include a material feed rate sensor 967 that senses the rate at which material is fed as it travels through the feeder housing 106, the clean grain elevator 130, or other locations within the harvester 100. Machine sensor 982 may include a biomass sensor 969 that senses the biomass traveling through the feeder housing 106, the separator 116, or other locations within the harvester 100.Machine sensor 982 may include a fuel consumption sensor 971 that senses the rate of fuel consumption of the harvester 100 over time. Machine sensor 982 may include a power utilization sensor 973 that senses power utilization in the harvester 100 (such as which subsystems are using power), the rate at which subsystems are using power, or the power distribution among the subsystems in the harvester 100. Machine sensor 982 may include a tire pressure sensor 977 that senses the inflation pressure in the tires 144 of the harvester 100. Machine sensor 982 may include a variety of other machine performance sensors or machine characteristic sensors (as shown in block 975). The machine performance sensors and machine characteristic sensors 975 can sense the machine performance or characteristics of the harvester 100.

[0136] While the crop material is being processed by the agricultural harvester 100, the harvest material property sensor 984 can sense the characteristics of the cut crop material. Crop properties may include things such as crop type, crop moisture content, grain quality (e.g., broken grain), MOG level, grain composition (e.g., starch and protein), MOG moisture content, and other crop material properties.

[0137] The 985 field and soil property sensor can sense the characteristics of fields and soils. Field and soil properties may include soil moisture, soil density, presence and location of waterlogging, soil type, and other soil and field characteristics.

[0138] The environmental characteristic sensor 987 can sense one or more environmental characteristics. Environmental characteristics may include things such as wind direction and speed, precipitation, fog, dust level, or other obstacles or other environmental features.

[0139] Figure 9A block diagram illustrating an example of a control zone generator 213 is shown. The control zone generator 213 includes a work machine actuator (WMA) selector 486, a control zone generation system 488, and a regime zone generation system 490. The control zone generator 213 may also include other items 492. The control zone generation system 488 includes a control zone standard identifier component 494, a control zone boundary definition component 496, a target setting identifier component 498, and other items 520. The regime zone generation system 490 includes a regime zone standard identifier component 522, a regime zone boundary definition component 524, a settings resolver identifier component 526, and other items 528. Before describing the overall operation of the control zone generator 213 in more detail, a brief description of some of the items in the control zone generator 213 and their corresponding operations will be provided first.

[0140] The agricultural harvester 100 or other operating machine may have various types of controllable actuators that perform different functions. The controllable actuators on the agricultural harvester 100 or other operating machine are collectively referred to as operating machine actuators (WMAs). Each WMA can be controlled independently based on values ​​on the functional prediction map, or WMAs can be controlled in groups based on one or more values ​​on the functional prediction map. Therefore, the control area generator 213 can generate control areas corresponding to each individual controllable WMA, or corresponding to groups of WMAs that are controlled in a coordinated manner.

[0141] WMA selector 486 selects the WMA or WMA group for which a corresponding control region is to be generated. Control region generation system 488 then generates a control region for the selected WMA or WMA group. For each WMA or WMA group, different criteria can be used to identify the control region. For example, for a WMA, the WMA response time can be used as a criterion for defining the boundaries of the control region. In another example, wear characteristics (e.g., the degree of wear of a particular actuator or mechanism due to its movement) can be used as a criterion for defining the boundaries of the control region. Control region criterion identifier component 494 identifies the specific criterion that will be used to define the control region for the selected WMA or WMA group. Control region boundary definition component 496 processes the values ​​on the functional prediction map in the analysis to define the boundaries of the control region on the functional prediction map based on the values ​​on the functional prediction map in the analysis and based on the control region criteria of the selected WMA or WMA group.

[0142] The target setting identifier component 498 sets the target setting value that will be used to control the WMA or WMA group in different control zones. For example, if the selected WMA is a header or other machine actuator 248, and the functional prediction map in the analysis is a functional prediction ear specification map 360 (with control zones) that maps the predicted ear specification values ​​of the diameter, length, or weight of indicator ears (e.g., corn ears) at different locations throughout the field, then the target setting in each control zone can be set based on the ear specification values ​​contained in the functional prediction ear specification map 360 within the identified control zone, such as the mulch position or mulch spacing. This is because, given the ear specification of the vegetation at a location in a field to be harvested by the agricultural harvester 100, controlling the position or spacing of the mulch of the agricultural harvester 100 so that the mulch has an appropriate setting is important for reducing losses and reducing the introduction of materials other than grain (MOG), etc.

[0143] In some examples, when controlling the harvester 100 based on its current or future position, multiple target settings are possible for the WMA at a given position. In this case, the target settings may have different values ​​and may compete with each other. Therefore, it is necessary to resolve the target settings so that only a single target setting is used to control the WMA. For example, in the case where the WMA is an actuator controlled in the propulsion system 250 to control the speed of the harvester 100, there may be multiple different competing sets of criteria, which are considered by the control zone generation system 488 when identifying the control zone and the target setting of the WMA selected in the control zone. For example, different target settings for controlling the position or spacing of the cover plate may be generated based on, for example, detected or predicted ear size values, detected or predicted operator command input values, detected or predicted yield values, detected or predicted vegetation index values, detected or predicted feed rate values, detected or predicted fuel efficiency values, detected or predicted grain loss values, or combinations of these values. It is important to note that these are merely examples, and the target settings used for various WMAs can be based on a variety of other values ​​or combinations of values. However, at any given time, the harvester 100 cannot simultaneously have multiple positions and spacing arrangements for the same set of covers. Instead, at any given time, the position or spacing of a set of covers on the harvester 100 is in a specific position or has a specific spacing. Therefore, one of the competing target settings is selected to control the position or spacing of the covers on the harvester 100.

[0144] Therefore, in some examples, the dynamic zone generation system 490 generates dynamic zones to resolve multiple different competing objective settings. The dynamic zone criterion identification component 522 identifies the criteria used to establish dynamic zones on the selected WMA or WMA group on the functional prediction map in the analysis. Some criteria that can be used to identify or define dynamic zones include, for example, ear size, operator command input, vegetation index value, yield value, and a variety of other criteria, such as crop type or crop species based on the planting map, or another source of crop type or crop species, weed type, weed density, or crop state (such as whether the crop is lodged, partially lodged, or upright), and any number of other criteria. These are just some examples of criteria that can be used to identify or define dynamic zones. Just as each WMA or WMA group may have a corresponding control zone, different WMAs or WMA groups may also have corresponding dynamic zones. The dynamic zone boundary definition component 524 identifies the boundaries of the dynamic zones on the functional prediction map in the analysis based on the dynamic zone criteria identified by the dynamic zone criterion identification component 522.

[0145] In some examples, dynamic zones may overlap. For instance, the ear specification dynamic zone may partially or completely overlap with the crop status dynamic zone. In such examples, different dynamic zones can be assigned priority levels such that, in the case of two or more overlapping dynamic zones, the dynamic zone assigned a higher priority level or importance takes precedence over the dynamic zone with a lower priority level or importance. The priority levels of dynamic zones can be set manually or automatically using rule-based systems, model-based systems, or other systems. As an example, in the case of an overlap between the ear specification dynamic zone and the crop status dynamic zone, the ear specification dynamic zone can be assigned greater importance in the priority level than the crop status dynamic zone, thus giving priority to the ear specification dynamic zone.

[0146] Furthermore, for a given WMA or WMA group, each dynamic region may have a unique setting resolver. The setting resolver identifier component 526 identifies a specific setting resolver for each dynamic region identified on the functional prediction graph in the analysis, and identifies a specific setting resolver for the selected WMA or WMA group.

[0147] Once a setting resolver is identified for a specific dynamic zone, it can be used to resolve competing target settings, where more than one target setting is identified based on the control zone. Different types of setting resolvers can take different forms. For example, a setting resolver for each dynamic zone may include a manually selected resolver, in which the competing target settings are presented to the operator or other user for resolution. In another example, the setting resolver may include a neural network or other artificial intelligence or machine learning system. In this case, the setting resolver can resolve competing target settings based on predicted or historical quality metrics corresponding to each of the different target settings. As an example, increasing the mantle spacing can reduce MOG introduction but increase grain loss at the header. Decreasing the mantle spacing can increase MOG introduction and thus reduce overall machine productivity. When selecting a quality metric, such as grain loss or machine productivity, given two competing mantle spacing settings, the predicted or historical value for the selected quality metric can be used to resolve conflicting settings for the WMA or WMA group. In some cases, the setting resolver may be a set of threshold rules that can be used to substitute for or supplement dynamic zones. An example of a threshold rule can be expressed as follows:

[0148] If the predicted ear size value is greater than x (where x is the selected or predetermined value) within 20 feet of the header at 100 meters from the harvester, the target setpoint selected based on header grain loss rather than other competing targets is used; otherwise, the target setpoint based on machine output rather than other competing targets is used.

[0149] A target parser can be a logical component that executes logical rules when identifying a target target. For example, a target parser can parse a target target while attempting to minimize harvest time, minimize total harvest cost, or maximize harvested grain, or other variables calculated as a function of different candidate target targets. Harvesting time can be minimized when the amount of harvested grain is reduced to or below a selected threshold. Total harvest cost can be minimized when the total harvest cost is reduced to or below a selected threshold. Harvested grain can be maximized when the amount of harvested grain is increased to or above a selected threshold.

[0150] Figure 9 This is a flowchart illustrating an example of the operation of the control region generator 213 when generating control regions and dynamic regions for a graph (e.g., a graph in analysis) received by the control region generator 213 for region processing.

[0151] At box 530, control area generator 213 receives the graph in the analysis for processing. In one example, as shown in box 532, the graph in the analysis is a functional prediction graph. For example, the graph in the analysis could be one of functional prediction graphs 436 or 440. In another example, the graph in the analysis could be a functional prediction spike specification graph 360. Box 534 indicates that the graph in the analysis can also be other graphs.

[0152] At box 536, WMA selector 486 selects the WMA or WMA group for which a control area will be generated on the graph in the analysis. At box 538, control area criterion identification component 494 obtains the control area defining criteria for the selected WMA or WMA group. Box 540 indicates an example where the control area criteria are or include the wear characteristics of the selected WMA or WMA group. Box 542 indicates an example where the control area defining criteria are or include the magnitude and variation of input source data, such as the magnitude and variation of values ​​on the graph in the analysis or the magnitude and variation of inputs from various field sensors 208. Box 544 indicates an example where the control area defining criteria are or include physical machine characteristics, such as the physical dimensions of the machine, the speed of operation of different subsystems, or other physical machine characteristics. Box 546 indicates an example where the control area defining criteria are or include the responsiveness of the selected WMA or WMA group when a setpoint for a new command is reached. Box 548 indicates an example where the control area defining criteria are or include machine performance metrics. Box 549 indicates an example where the control zone boundary criterion is time-based, meaning that the harvester 100 will not cross the boundary of the control zone until a selected amount of time has elapsed since the harvester 100 entered the specific control zone. In some cases, the selected amount of time may be a minimum amount of time. Therefore, in some situations, the control zone boundary criterion can prevent the harvester 100 from crossing the boundary of the control zone until at least the selected amount of time has elapsed. Box 550 indicates an example where the control zone boundary criterion is or includes operator preference. Box 551 indicates an example where the control zone boundary criterion is based on a selected size value. For example, a control zone boundary criterion based on a selected size value can exclude the definition of control zones smaller than the selected size. In some cases, the selected size may be a minimum size. Box 552 indicates an example where the control zone boundary criterion is also or includes other items.

[0153] At box 554, the dynamic zone criterion identification component 522 obtains the dynamic zone limiting criteria for the selected WMA or WMA group. Box 556 indicates an example where the dynamic zone limiting criteria are based on manual input from operator 260 or another user. Box 558 shows an example where the dynamic zone limiting criteria are based on ear specification values. Box 560 shows an example where the dynamic zone limiting criteria are based on vegetation index values. Box 561 shows an example where the dynamic zone limiting criteria are based on sowing characteristic values. Box 562 shows an example where the dynamic zone limiting criteria are based on yield values. Box 564 indicates an example where the dynamic zone limiting criteria are also or include other criteria, such as soil type, crop type or crop variety, weed type, weed density, or crop condition (e.g., whether the crop is lodged). Other criteria may also be used.

[0154] At box 566, the control area boundary defining component 496 generates the boundary of the control area on the graph in the analysis based on the control area criteria. The dynamic area boundary defining component 524 generates the boundary of the dynamic area on the graph in the analysis based on the dynamic area criteria. Box 568 indicates an example where the boundaries of the control area and the dynamic area are identified. Box 570 shows that the target setting identifier component 498 identifies the target setting for each in the control area. The control area and the dynamic area can also be generated in other ways, and this is indicated by box 572.

[0155] At box 574, the set parser identifier component 526 identifies the set parser for the selected WMA in each dynamic region defined by the dynamic region boundary defining component 524. As discussed above, the dynamic region parser can be a human parser 576, an artificial intelligence or machine learning system parser 578, a parser 580 based on the predicted quality or historical quality of each competing objective setting, a rule-based parser 582, a performance criterion-based parser 584, or another parser 586.

[0156] At box 588, WMA selector 486 determines if there are more WMAs or WMA groups to process. If there are additional WMAs or WMA groups to process, processing returns to box 536, where the next WMA or WMA group to define the control area and dynamic area for is selected. When no additional WMAs or WMA groups remain to generate control areas or dynamic areas for, processing moves to box 590, where control area generator 213 generates a graph of control area, target setting, dynamic area, and setting resolver for each output in each WMA or WMA group. As discussed above, the output graph can be presented to operator 260 or another user; the output graph can be provided to control system 214; or the output graph can be output in other ways.

[0157] Figure 11 An example is shown of a control system 214 controlling the operation of an agricultural harvester 100 based on a map output by a control zone generator 213. Thus, at box 592, the control system 214 receives a map of the work site. In some cases, this map may be a functional prediction map that includes control zones and dynamic zones (as shown in box 594). In some cases, the received map may be a functional prediction map that excludes control zones and dynamic zones. Box 596 indicates an example where the received work site map may be a priori information map with control zones and dynamic zones identified on that map. Box 598 indicates an example where the received map may include multiple different maps or multiple different layers. Box 610 indicates an example where the received map may also take other forms.

[0158] At box 612, the control system 214 receives sensor signals from the geolocation sensor 204. The sensor signals from the geolocation sensor 204 may include data indicating the geolocation 614 of the harvester 100, the speed 616 of the harvester 100, the heading 618 of the harvester 100, or other information 620. At box 622, the area controller 247 selects a dynamic area, and at box 624, the area controller 247 selects a control area on the map based on the geolocation sensor signals. At box 626, the area controller 247 selects a WMA or WMA group to be controlled. At box 628, the area controller 247 obtains one or more target settings for the selected WMA or WMA group. The target settings obtained for the selected WMA or WMA group can come from a variety of different sources. For example, box 630 shows an example where one or more of the target settings for the selected WMA or WMA group are based on inputs from a control area on a map from the work site. Box 632 illustrates an example where one or more target settings are obtained from manual input by operator 260 or another user. Box 634 illustrates an example where target settings are obtained from field sensors 208. Box 636 illustrates an example where one or more target settings are obtained from sensors on other machines operating simultaneously with agricultural harvester 100 in the same field, or from sensors on machines that have previously operated in the same field. Box 638 illustrates an example where target settings are also obtained from other sources.

[0159] At box 640, the zone controller 247 accesses the setpoint resolver of the selected dynamic zone and controls the setpoint resolver to resolve competing target settings into a resolved target setting. As discussed above, in some cases, the setpoint resolver may be a manual resolver, in which case the zone controller 247 controls the operator interface mechanism 218 to present competing target settings to the operator 260 or another user for resolution. In some cases, the setpoint resolver may be a neural network or other artificial intelligence or machine learning system, and the zone controller 247 submits competing target settings to the neural network, artificial intelligence, or machine learning system for selection. In some cases, the setpoint resolver may be based on predicted or historical quality metrics, threshold rules, or logical components. In any of these later examples, the zone controller 247 executes the setpoint resolver to obtain a resolved target setting based on predicted or historical quality metrics, threshold rules, or, when using logical components.

[0160] At block 642, if the zone controller 247 has identified the resolved target setting, it provides the resolved target setting to other controllers in the control system 214. These controllers generate control signals based on the resolved target setting and apply these control signals to the selected WMA or WMA group. For example, if the selected WMA is a machine or header actuator 248, the zone controller 247 provides the resolved target setting to the setting controller 232 or the header / actual controller 238, or both, to generate control signals based on the resolved target setting, and those generated control signals are applied to the machine or header actuator 248. At block 644, if an additional WMA or additional WMA group is to be controlled at the current geographic location of the harvester 100 (as detected in block 612), the process returns to block 626, where the next WMA or WMA group is selected. The process represented by blocks 626 through 644 continues until all WMAs or WMA groups to be controlled at the current geographic location of the harvester 100 have been resolved. If no additional WMA or WMA group remains to be controlled at the current geographic location of the harvester 100, the process proceeds to box 646, where the area controller 247 determines whether an additional control area to be considered exists in the selected dynamic area. If an additional control area to be considered exists, the process returns to box 624, where the next control area is selected. If no additional control area needs to be considered, the process proceeds to box 648, where it is determined whether any additional dynamic areas remain to be considered. The area controller 247 determines whether any additional dynamic areas remain to be considered. If any additional dynamic areas remain to be considered, the process returns to box 622, where the next dynamic area is selected.

[0161] At box 650, the zone controller 247 determines whether the operation being performed by the harvester 100 has been completed. If not, the zone controller 247 determines whether control zone criteria have been met to continue processing, as shown in box 652. For example, as mentioned above, control zone defining criteria may include criteria that define when the harvester 100 can cross the control zone boundary. For example, whether the harvester 100 can cross the control zone boundary may be defined by a selected time period, meaning that the harvester 100 is prevented from crossing the zone boundary until the selected amount of time has elapsed. In this case, at box 652, the zone controller 247 determines whether the selected time period has elapsed. Additionally, the zone controller 247 can perform processing continuously. Therefore, the zone controller 247 does not wait for any specific time period before continuing to determine whether the operation of the harvester 100 has been completed. At box 652, if the zone controller 247 determines that it is time to continue processing, then processing continues at box 612, where the zone controller 247 again receives input from the geolocation sensor 204. It should also be understood that the zone controller 247 can use a multiple-input multiple-output controller to control the WMA and WMA group simultaneously, rather than controlling the WMA and WMA group sequentially.

[0162] Figure 12 This is a block diagram illustrating an example of an operator interface controller 231. In the example shown, the operator interface controller 231 includes an operator input command processing system 654, other controller interaction systems 656, a voice processing system 658, and a motion signal generator 660. The operator input command processing system 654 includes a voice management system 662, a touch gesture management system 664, and other items 666. The other controller interaction system 656 includes a controller input processing system 668 and a controller output generator 670. The voice processing system 658 includes a trigger detector 672, a recognition component 674, a synthesis component 676, a natural language understanding system 678, a dialogue management system 680, and other items 682. The motion signal generator 660 includes a visual control signal generator 684, an audio control signal generator 686, a haptic control signal generator 688, and other items 690. Figure 12 Before managing various operator interface actions, the example operator interface controller 231 shown in the figure first provides a brief description of some items in the operator interface controller 231 and their associated operations.

[0163] The operator input command processing system 654 detects operator input on the operator interface mechanism 218 and processes these command inputs. The voice management system 662 detects voice input and manages interaction with the voice processing system 658 to process voice command inputs. The touch gesture management system 664 detects touch gestures on touch-sensitive elements in the operator interface mechanism 218 and processes these command inputs.

[0164] Other controller interaction system 656 manages and interacts with other controllers in control system 214. Controller input processing system 668 detects and processes inputs from other controllers in control system 214, and controller output generator 670 generates outputs and provides these outputs to other controllers in control system 214. Voice processing system 658 recognizes voice inputs, determines the meaning of these inputs, and provides outputs indicating the meaning of the voice inputs. For example, voice processing system 658 can recognize voice input from operator 260 as a setting change command in which operator 260 is instructing control system 214 to change the setting of controllable subsystem 216. In such an example, voice processing system 658 recognizes the content of the voice command, identifies the meaning of the command as a setting change command, and returns the meaning of the input to voice management system 662. Voice management system 662 then interacts with controller output generator 670 to provide command outputs to the appropriate controllers in control system 214 to complete the voice setting change command.

[0165] The voice processing system 658 can be invoked in various ways. For example, in one example, the voice management system 662 continuously provides input from a microphone (as part of the operator interface mechanism 218) to the voice processing system 658. The microphone detects speech from the operator 260, and the voice management system 662 provides the detected speech to the voice processing system 658. A trigger detector 672 detects a trigger indicating that the voice processing system 658 has been invoked. In some cases, when the voice processing system 658 receives continuous voice input from the voice management system 662, the voice recognition component 674 performs continuous speech recognition on all speech uttered by the operator 260. In some cases, the voice processing system 658 is configured to be invoked using a wake-up word. That is, in some cases, the operation of the voice processing system 658 can be initiated based on the recognition of a selected speech word (referred to as a wake-up word). In such an example, when the recognition component 674 recognizes the wake-up word, the recognition component 674 provides an indication to the trigger detector 672 that the wake-up word has been recognized. Trigger detector 672 detects that voice processing system 658 has been invoked or triggered by a wake-up word. In another example, voice processing system 658 may be invoked by operator 260 actuating an actuator on the user interface mechanism, such as by touching an actuator on a touch-sensitive display, by pressing a button, or by providing another trigger input. In such an example, trigger detector 672 can detect that voice processing system 658 has been invoked when a trigger input via the user interface mechanism is detected. Trigger detector 672 may also detect that voice processing system 658 has been invoked in other ways.

[0166] Once the speech processing system 658 is invoked, speech input from operator 260 is provided to speech recognition component 674. Speech recognition component 674 recognizes linguistic elements in the speech input, such as words, phrases, or other linguistic units. Natural language understanding system 678 identifies the meaning of the recognized speech. This meaning can be any of the following: natural language output, command output identifying a command reflected in the recognized speech, value output identifying a value in the recognized speech, or a variety of other outputs reflecting an understanding of the recognized speech. For example, more generally, natural language understanding system 678 and speech processing system 568 can understand the meaning of speech recognized in the environment of agricultural harvester 100.

[0167] In some examples, the speech processing system 658 can also generate output that guides the operator 260 through a voice-based user experience. For example, the dialogue management system 680 can generate and manage dialogues with the user to identify what the user wants to do. This dialogue can disambiguate user commands, identify one or more specific values ​​required to execute the user command, or obtain or provide other information from the user, or both. The synthesis component 676 can generate speech synthesis that can be presented to the user through an audio operator interface mechanism such as a speaker. Therefore, the dialogue managed by the dialogue management system 680 can be exclusively verbal, or a combination of visual and verbal dialogue.

[0168] Motion signal generator 660 generates motion signals to control operator interface mechanism 218 based on outputs from one or more of the operator input command processing system 654, other controller interaction system 656, and voice processing system 658. Visual control signal generator 684 generates control signals to control visual items in operator interface mechanism 218. Visual items may be lights, displays, warning indicators, or other visual items. Audio control signal generator 686 generates outputs to control audio elements of operator interface mechanism 218. Audio elements include speakers, audible alarm mechanisms, horns, or other audible elements. Tactile control signal generator 688 generates control signals that are output to control tactile elements of operator interface mechanism 218. Tactile elements include vibratory elements that can be used to make vibrations, such as an operator's seat, steering wheel, pedals, or joystick used by the operator. Tactile elements may include tactile feedback or force feedback elements that provide tactile feedback or force feedback to the operator through the operator interface mechanism. Tactile elements may also include a variety of other tactile elements.

[0169] Figure 13 This is a flowchart illustrating an example of the operation of the operator interface controller 231 when generating an operator interface display unit on an operator interface mechanism 218 that may include a touch-sensitive display screen. Figure 13 An example of how the operator interface controller 231 can detect and process operator interactions with the touch-sensitive display is also shown.

[0170] At box 692, operator interface controller 231 receives a graph. Box 694 indicates that the graph is an example of a functional prediction graph, while box 696 indicates that the graph is an example of another type of graph. At box 698, operator interface controller 231 receives input from geolocation sensor 204 identifying the geolocation of harvester 100. As shown in box 700, the input from geolocation sensor 204 may include the heading and position of harvester 100. Box 702 indicates that the input from geolocation sensor 204 includes an example of the speed of harvester 100, and box 704 indicates that the input from geolocation sensor 204 includes an example of other items.

[0171] At box 706, the vision control signal generator 684 in the operator interface controller 231 controls the touch-sensitive display in the operator interface mechanism 218 to generate a display showing all or part of the field represented by the received map. Box 708 indicates that the displayed field may include a current position marker indicating the current position of the harvester 100 relative to the field. Box 710 indicates an example where the displayed field includes a next work unit marker that identifies the next work unit (or area on the field) in which the harvester 100 will operate. Box 712 indicates an example where the displayed field includes an upcoming area display showing areas not yet processed by the harvester 100, and box 714 indicates an example where the displayed field includes a previously visited display representing areas of the field that the harvester 100 has already processed. Box 716 indicates an example where the field shown displays multiple characteristics of the field that are georeferenced on the map. For example, if the received map is a predicted ear specification map (e.g., a functional predicted ear specification map 360), the displayed field may show the ear specification values ​​georeferenced within the displayed field. In other examples, the received map may be another map of the map described herein. Thus, the displayed field may show different characteristic values ​​georeferenced within the displayed field, such as yield values, vegetation index values, seeding characteristic values, or operator command values, as well as a variety of other values. Mapped characteristics may be shown in previously visited areas (as shown in box 714), upcoming areas (as shown in box 712), and the next work unit (as shown in box 710). Box 718 indicates an example where the displayed field also includes other items.

[0172] Figure 14This illustration shows an example of a user interface display 720 that can be generated on a touch-sensitive display screen. In other embodiments, the user interface display 720 can be generated on other types of displays. The touch-sensitive display screen can be installed in the operator's compartment of the agricultural harvester 100 or on mobile equipment or elsewhere. (Continuing the description...) Figure 13 Before the flowchart shown, the user interface display unit 720 will be described.

[0173] exist Figure 14 In the example shown, the user interface display 720 illustrates a touch-sensitive display including display features for operating a microphone 722 and a speaker 724. Therefore, the touch-sensitive display can be communicatively connected to the microphone 722 and the speaker 724. Box 726 indicates that the touch-sensitive display may include various user interface control actuators, such as buttons, keypads, softkeys, links, icons, switches, etc. The operator 260 can actuate the user interface control actuators to perform various functions.

[0174] exist Figure 14 In the example shown, the user interface display 720 includes a field display portion 728 that displays at least a portion of the field in which a harvester 100 is operating. The field display portion 728 is shown with a current position marker 708 corresponding to the current position of the harvester 100 within the portion of the field shown in the field display portion 728. In one example, the operator can control a touch-sensitive display to zoom in on a portion of the field display portion 728, or pan or scroll the field display portion 728 to display different portions of the field. The next work unit 730 is shown as the area of ​​the field directly in front of the current position marker 708 of the harvester 100. The current position marker 708 can also be configured to identify the direction of travel of the harvester 100, the speed of travel of the harvester 100, or both. Figure 14 In the image, the shape of the current position marker 708 provides an indication of the orientation of the agricultural harvester 100 in the field, which can be used as an indication of the direction of travel of the agricultural harvester 100.

[0175] The size of the next work unit 730, marked on the field display section 728, can vary based on a variety of different criteria. For example, the size of the next work unit 730 can vary based on the travel speed of the harvester 100. Therefore, when the harvester 100 travels faster, the area of ​​the next work unit 730 can be larger than if the harvester 100 travels slower. In another example, the size of the next work unit 730 can vary based on the size of the harvester 100 (including equipment on the harvester 100, such as the header 102). For example, the width of the next work unit 730 can vary based on the width of the header 102. The field display section 728 is also shown displaying previously visited areas 714 and upcoming areas 712. The previously visited area 714 represents an area that has already been harvested, while the upcoming area 712 represents an area that still needs to be harvested. The field display section 728 is also shown displaying different characteristics of the field. Figure 14 In the example shown, the graph being displayed is a predicted ear specification graph, such as a functional predicted ear specification graph 360. Therefore, multiple different ear specification markers are displayed on the field display section 728. A set of ear specification display markers 732 is shown to exist in the already visited area 714. A set of ear specification display markers 732 is also shown to exist in the upcoming area 712, and a set of ear specification display markers 732 is shown to exist in the next work unit 730. Figure 14 The ear size display mark 732 is shown to consist of different symbols indicating areas with similar ear sizes. Figure 14 In the example shown, the "!" symbol indicates an area with a large ear diameter; the "*" symbol indicates an area with a medium ear diameter; and the "#" symbol indicates an area with a small ear diameter. Ear diameter is just one example; other specifications of the ear, such as length or weight, can also be displayed. Therefore, the field display section 728 displays different measured or predicted values ​​(or characteristics indicated by said values) located in different areas of the field, and uses various display markers 732 to represent those measured or predicted values ​​(or characteristics indicated or derived from said values). As shown, the field display section 728 includes display markers at specific locations associated with a specific location on the field being displayed, in particular... Figure 14 The example shown includes a spikelet specification display mark 732. In some cases, each location of the field may have a display mark associated with that location. Therefore, in some cases, a display mark may be provided at each location of the field display section 728 to identify an attribute that maps a characteristic to each particular location of the field. Thus, this disclosure includes providing spikelet specification display marks 732 (as shown in the example) at one or more locations on the field display section 728. Figure 14In the context of this example, display marks (as described above) are used to identify the attributes, degree, etc., of the characteristic being displayed, thereby identifying the characteristic at a corresponding location in the field being displayed. As previously mentioned, display marks 732 can consist of different symbols, and as described below, these symbols can be any display feature, such as different colors, shapes, patterns, intensities, text, icons, or other display features. In some cases, each location in the field can have a display mark associated with that location. Therefore, in some cases, display marks can be provided at each location of the field display section 728 to identify the nature of the characteristic mapped to each particular location in the field. Thus, this disclosure covers providing display marks at one or more locations on the field display section 728, such as ear specification display marks 732 (as described above). Figure 14 (As in the background of this example) to identify the nature, degree, etc. of the characteristic being displayed, thereby identifying the characteristic at the corresponding location in the field being displayed.

[0176] In other examples, the graph being displayed can be one or more of the graphs described herein, including infographics, prior infographics, functional predictive graphs such as predictive graphs or predictive control area graphs, or combinations thereof. Therefore, the labels and characteristics being displayed will be associated with the information, data, characteristics, and values ​​provided by the one or more graphs being displayed.

[0177] exist Figure 14 In the example, the user interface display 720 also has a control display section 738. The control display section 738 allows the operator to view information and interact with the user interface display 720 in various ways.

[0178] The actuators and display markers in display section 738 can be displayed as, for example, individual items, fixed lists, scrollable lists, drop-down menus, or drop-down lists. Figure 14 In the example shown, display portion 738 displays information for three different ear size categories corresponding to the three symbols mentioned above. Display portion 738 also includes a set of touch-sensitive actuators that operator 260 can interact with by touch. For example, operator 260 can touch the touch-sensitive actuator with a finger to activate the corresponding touch-sensitive actuator.

[0179] like Figure 14As shown, display portion 738 includes an interactive sign display portion indicated approximately at 741. Interactive sign display portion 741 includes a sign bar 739 that displays signs that have been set automatically or manually. Sign actuator 740 allows operator 260 to mark locations (e.g., the current location of the harvester, or another location on the field specified by the operator) and add information indicating characteristics found at the current location (e.g., ear size). For example, when operator 260 actuates sign actuator 740 by touching it, touch gesture management system 664 in operator interface controller 231 identifies the current location as a location where harvester 100 encounters a large ear diameter. When operator 260 touches button 742, touch gesture management system 664 identifies the current location as a location where harvester 100 encounters a medium ear diameter. When operator 260 touches button 744, touch gesture management system 664 identifies the current location as a location where harvester 100 encounters a small ear diameter. When one of the sign actuators 740, 742, or 744 is actuated, the touch gesture management system 664 can control the visual control signal generator 684 to add a symbol corresponding to the identified characteristic on the field display portion 728 at the user-identified location. In this way, areas of the field where predicted values ​​cannot accurately identify actual values ​​can be marked for later analysis or for machine learning. In other examples, an operator can specify an area in front of or around the harvester 100 by actuating one of the sign actuators 740, 742, or 744, allowing control of the harvester 100 based on values ​​specified by the operator 260.

[0180] Display section 738 also includes an interactive marker display section indicated approximately at 743. Interactive marker display section 743 includes a symbol bar 746 that displays the value or characteristic of each category tracked on field display section 728 (in...). Figure 14 In the case of a spikelet specification, the symbol corresponding to the spikelet specification is used. Display section 738 also includes an interactive specification display section indicated approximately at 745. Interactive specification display section 745 includes a specification bar 748 that displays the pair of values ​​or properties (in...) Figure 14 In the case of ear size, the designator (which can be a text designator or other designator) is used to identify the category. Without limitation, the symbols in the symbol bar 746 and the designators in the designator bar 748 can include any display features, such as different colors, shapes, patterns, intensities, text, icons or other display features, and can be customized through the interaction of the operator of the agricultural harvester 100.

[0181] Display section 738 also includes an interactive value display section indicated approximately at 747. Interactive value display section 747 includes a value display bar 750 displaying the selected value. The selected value corresponds to a characteristic or value, or both, being tracked or displayed on field display section 728. The selected value can be selected by the operator of the agricultural harvester 100. The selected value in value display bar 750 defines a range of values ​​or, by virtue of, categorizes other values ​​(e.g., predicted values). Therefore, in Figure 14 In the examples, predicted or measured ear diameters of 2.6 inches or greater are classified as "large ear diameters," while predicted or measured ear diameters of 1.4 inches or less are classified as "small ear diameters." In some examples, the selected values ​​may include a range, such that predicted or measured values ​​within the selected range will be classified under the corresponding designator. For example... Figure 14 As shown, "medium ear diameter" includes a range of 1.41 inches to 2.59 inches, such that predicted or measured ear diameters falling within this range are classified as "medium ear diameter". The value selected in the value display bar 750 can be adjusted by the operator of the agricultural harvester 100. In one example, the operator 260 can select a specific portion of the field display section 728, and the value displayed in bar 750 will be for that specific portion. Therefore, the value in bar 750 can correspond to the values ​​in display sections 712, 714, or 730.

[0182] Display section 738 also includes an interactive threshold display section indicated approximately at 749. Interactive threshold display section 749 includes a threshold display bar 752 that displays action thresholds. The action threshold in bar 752 can be a threshold corresponding to a selected value in value display bar 750. If the predicted or measured value of the characteristic being tracked or displayed, or both, meets the corresponding action threshold in threshold display bar 752, the control system 214 takes the action identified in bar 754. In some cases, the measured or predicted value can satisfy the corresponding action threshold by reaching or exceeding it. In one example, operator 260 can select a threshold, for example, to change the threshold by touching it in threshold display bar 752. Once selected, operator 260 can change the threshold. The threshold in bar 752 can be configured such that a specified action is performed when the measured or predicted value of the characteristic exceeds, is equal to, or is less than the threshold. In some cases, a threshold may represent a range of values, or a range of deviations from the value selected in value display bar 750, such that predicted or measured characteristic values ​​that reach or fall within that range satisfy the threshold. For example, in the ear size example, a predicted ear size value falling within 10% of 2.6 inches would satisfy the corresponding action threshold (within 10% of 2.6 inches), and control system 214 would take an action such as increasing the cover spacing. In other examples, the threshold in threshold display bar 752 is separated from the selected value in value display bar 750, such that the value in value display bar 750 defines the classification and display of predicted or measured values, while the action threshold defines when an action is taken based on the measured or predicted value. For example, while a predicted or measured ear diameter of 2.0 inches might be designated as "medium ear diameter" for classification and display purposes, the action threshold could be 2.1 inches, such that no action is taken until the ear diameter meets that threshold. In other examples, the threshold in threshold display bar 752 may include distance or time. For example, in the example of distance, the threshold could be a threshold distance from the field to an area where the measured or predicted value is georeferenced, such that the harvester 100 must be in that area before taking action. For example, a threshold distance value of 5 feet means that action will be taken when the harvester is located 5 feet or less from the field where the measured or predicted value is georeferenced. In the example where the threshold is time, the threshold could be a threshold time for the harvester 100 to reach the field where the measured or predicted value is georeferenced. For example, a threshold of 5 seconds means that action will be taken when the harvester 100 is 5 seconds away from the field where the measured or predicted value is georeferenced. In such examples, the harvester's current position and travel speed can be considered.

[0183] Display portion 738 also includes an interactive action display portion indicated approximately at 751. Interactive action display portion 751 includes an action display bar 754 displaying action identifiers that indicate the action to be taken when a predicted or measured value meets an action threshold in threshold display bar 752. Operator 260 can touch the action identifier in said bar 754 to change the action to be taken. An action can be taken when the threshold is met. For example, at the bottom of bar 754, if the measured or predicted value meets the threshold in bar 752, increasing the cover spacing action and decreasing the cover spacing action are identified as actions to be taken. In some examples, multiple actions can be taken when the threshold is reached. For example, the cover spacing can be adjusted, the power or drive output to the straw processing assembly (e.g., straw roller, collection chain) can be adjusted, and the speed of the agricultural machinery can be adjusted. These are merely some examples.

[0184] The actions that can be set in column 754 can be any of a variety of different types of actions. For example, these actions can include a prohibition action that, when executed, prevents the combine harvester 100 from harvesting further in an area. These actions can include a speed-changing action that, when executed, changes the speed at which the combine harvester 100 travels through the field. These actions can include setting-changing actions for changing the settings of an internal actuator or another WMA or WMA group, or setting-changing actions for implementing changes to the settings of one or more sets of manhole covers (e.g., manhole spacing) and a variety of other settings. These are merely examples, and a wide variety of other actions are considered herein.

[0185] Items displayed on the user interface display 720 can be visually controlled. Visual control of the interface display 720 can be performed to capture the attention of the operator 260. For example, the intensity, color, or pattern of the displayed item can be modified. Additionally, the item can be controlled to blink. As an example, a description of changes to the visual appearance of the item is provided. Therefore, other aspects of the visual appearance of the item can be changed. Thus, items can be modified in a desired manner in various situations to, for example, capture the attention of the operator 260. Furthermore, while a specific number of items are displayed on the user interface display 720, this is not necessary. In other examples, more or fewer items, or more or fewer specific items, can be included on the user interface display 720.

[0186] Now back Figure 13The flowchart continues to describe the operation of the operator interface controller 231. At block 760, the operator interface controller 231 detects an input setting a marker and controls the touch-sensitive user interface display 720 to display the marker on the field display section 728. The detected input can be operator input (as shown at 762) or input from another controller (as shown at 764). At block 766, the operator interface controller 231 detects a field sensor input indicating a measured characteristic of the field from one of the field sensors 208. At block 768, the vision control signal generator 684 generates a control signal to control the user interface display 720 to display actuators for modifying the user interface display 720 and for modifying machine control. For example, block 770 indicates that one or more actuators for setting or modifying values ​​in columns 739, 746, and 748 can be displayed. Therefore, the user can set markers and modify the characteristics of these markers. For example, the user can modify the ear specification level and ear specification designator corresponding to the marker. Box 772 indicates that the action threshold in column 752 is displayed. Box 776 indicates that the action in column 754 is displayed, and box 778 indicates that the selected value in column 750 is displayed. Box 780 indicates that various other information and actuators can also be displayed on the user interface display unit 720.

[0187] At box 782, the operator input command processing system 654 detects and processes operator input corresponding to the interaction with the user interface display unit 720 performed by the operator 260. If the user interface mechanism displayed on the user interface display unit 720 is a touch-sensitive display screen, the interactive input performed by the operator 260 with the touch-sensitive display screen can be a touch gesture 784. In some cases, the operator interactive input can be input using a clicking device 786 or other operator interactive input device 788.

[0188] At box 790, the operator interface controller 231 receives a signal indicating an alarm condition. For example, box 792 indicates a signal that can be received by the controller input processing system 668, indicating that the detected or predicted value meets a threshold condition present in column 752. As previously explained, a threshold condition can include a value below a threshold, a value at a threshold, or a value above a threshold. Box 794 shows that the action signal generator 660 can, in response to receiving an alarm condition, generate a visual alarm using the visual control signal generator 684, an audio alarm using the audio control signal generator 686, a tactile alarm using the tactile control signal generator 688, or any combination thereof, to alert the operator 260. Similarly, as shown in box 796, the controller output generator 670 can generate outputs to other controllers in the control system 214, causing these controllers to perform the corresponding actions identified in column 754. Box 798 shows that the operator interface controller 231 can also detect and process alarm conditions in other ways.

[0189] Box 900 illustrates that the voice management system 662 can detect and process input that invokes the voice processing system 658. Box 902 illustrates that performing voice processing may include using the dialogue management system 680 to converse with the operator 260. Box 904 illustrates that voice processing may include providing signals to the controller output generator 670 to automatically perform control operations based on voice input.

[0190] Table 1 below shows an example of a dialogue between the operator interface controller 231 and the operator 260. In Table 1, the operator 260 invokes the speech processing system 658 using a trigger word or wake-up word detected by the trigger detector 672. In the example shown in Table 1, the wake-up word is "Johnny".

[0191] Table 1

[0192] Operator: "Johnny, tell me about the current ear size value."

[0193] Operator interface controller: "At the current position, the ear diameter is large."

[0194] Operator: "Johnny, what should I do regarding the grade level of this ear of grain?"

[0195] Operator interface controller: "Increase the spacing between the cover plates on the cutting table."

[0196] Table 2 illustrates such an example where the speech synthesis component 676 provides output to the audio control signal generator 686 to provide audio updates intermittently or periodically. The interval between updates can be based on time (such as every five minutes), or on coverage or distance (such as every five acres), or on anomalies (such as when a measured value exceeds a threshold).

[0197] Table 2

[0198] Operator interface controller: "In the past 10 minutes, the operation has been in the medium-sized spike level area for 95% of the time."

[0199] Operator interface controller: "The next acre includes 66% medium ear size level and 33% small ear size level."

[0200] Operator interface controller: "Warning: Approaching a large ear size area. Adjust cover plate spacing."

[0201] Operator interface controller: "Note: Approaching the small ear size horizontal area, adjust the cover plate spacing."

[0202] The examples shown in Table 3 illustrate some actuators or user input mechanisms on the touch-sensitive display 720 that can be supplemented by voice dialogue. The examples in Table 3 also show that the motion signal generator 660 can generate motion signals to automatically mark areas of large ear size in a field being harvested.

[0203] Table 3

[0204] Human: "Johnny, mark the large ear size horizontal area."

[0205] Operator interface controller: "Large ear size horizontal areas have been marked."

[0206] The example shown in Table 4 illustrates that the action signal generator 660 can communicate with the operator 260 to start and end the marking of large ear-size horizontal areas.

[0207] Table 4

[0208] Human: "Johnny, begin marking the large ear size horizontal areas."

[0209] Operator interface controller: "Mark large ear size horizontal area".

[0210] Human: "Johnny, stop marking the large ear size horizontal area."

[0211] Operator interface controller: "Stop marking the large ear size horizontal area".

[0212] The example shown in Table 5 illustrates that the action signal generator 160 can generate signals for marking ear size level regions in a manner different from that shown in Tables 3 and 4.

[0213] Table 5

[0214] Human: "Johnny, mark the next 100 feet as the large ear specification horizontal area."

[0215] Operator interface controller: "The next 100 feet is marked as the large ear specification horizontal area."

[0216] Return again Figure 13 Box 906 shows that the operator interface controller 231 can also detect and process situations for outputting messages or other information in other ways. For example, the other controller interaction system 656 can detect input from other controllers indicating alarms or output messages that should be presented to the operator 260. Box 908 shows that the output can be an audio message. Box 910 shows that the output can be a visual message, and Box 912 shows that the output can be a tactile message. Until the operator interface controller 231 determines that the current harvesting operation is complete (as shown in Box 914), the processing returns to Box 698, where the geographical location of the harvester 100 is updated, and the processing continues as described above to update the user interface display 720.

[0217] Once the operation is complete, any desired values ​​displayed or already displayed on the user interface display unit 720 can be saved. These values ​​can also be used in machine learning to improve different parts of the predictive model generator 210, predictive map generator 212, control area generator 213, control algorithm, or other projects. The saved desired values ​​are indicated by box 916. These values ​​can be saved locally on the agricultural harvester 100, or they can be saved at a remote server location or sent to another remote system.

[0218] Thus, one or more maps are obtained by the agricultural harvester, showing agricultural characteristic values ​​at different geographical locations in the field being harvested. Field sensors on the harvester sense characteristics with values ​​indicating agricultural characteristics (such as ear size or operator commands) as the harvester moves through the field. A predictive map generator produces predictive maps that predict control values ​​at different locations in the field based on values ​​in the prior information maps and the agricultural characteristics sensed by the field sensors. The control system controls the controllable subsystems based on the control values ​​in the predictive maps.

[0219] A control value is a value upon which an action can be based. As described herein, a control value can include any value (or a characteristic indicated by or derived from that value) that can be used to control the agricultural harvester 100. A control value can be any value indicating an agricultural characteristic. A control value can be a predicted value, a measured value, or a detected value. A control value can include any value provided by a graph (such as any of the graphs described herein), for example, a control value can be a value provided by an infographic, a value provided by a priori infographic, or a value provided by a predictive graph (e.g., a functional predictive graph). A control value can also include any characteristic indicated by a value detected by any of the sensors described herein, or any characteristic derived from a detected value. In other examples, control values ​​can be provided by the operator of the agricultural machine, such as commands entered by the operator of the agricultural machine.

[0220] Processors and servers have been mentioned in this discussion. 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 by and facilitate the function of other components or items in these systems.

[0221] Furthermore, numerous user interface displays have been discussed. These displays can take various forms and can have various user-actuable operator interface mechanisms mounted on 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, they 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 mechanism. Moreover, voice recognition functionality can be used to actuate the mechanism using voice commands. Voice recognition can be implemented using voice detection devices (e.g., microphones) and software for recognizing the detected voice and executing commands based on the received voice.

[0222] Many data storage devices are also discussed. It should be noted that each data storage device 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 device; one or more data storage devices may all be located remotely from the system utilizing the data storage device; or one or more data storage devices may be local while the others are remote. This disclosure considers all of these configurations.

[0223] Furthermore, the accompanying diagram shows multiple boxes, with functionality belonging to each box. It should be noted that fewer boxes can be used to illustrate that functionality attributed to multiple different boxes is performed by fewer components. Moreover, more boxes can be used to show that the functionality can be distributed across more components. In different examples, some functionality can be added, and some functionality can be removed.

[0224] 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 components, some of which are described below, performing the functions associated with those systems, components, logic, or interactions. Furthermore, any or all of such systems, components, logic, and interactions can be implemented by software loaded into memory and subsequently executed by a processor, server, or other computing component, as described below. Any or all of such 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.

[0225] Figure 15 This is a block diagram of an 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 that do not require the end user to know the physical location or configuration of the system delivering the services. In various examples, the remote server can deliver the 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 through a web browser or any other computing component. Figure 2The software or components shown herein, along with 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 shared data centers, even if the service appears as a single access point for a user. Therefore, the components and functionalities described herein can be provided from remote servers at remote locations using a remote server architecture. Alternatively, the components and functionalities can be provided from a server, or they can be installed directly or otherwise on client devices.

[0226] exist Figure 15 In the examples shown, some items are similar to Figure 2 The items shown are numbered similarly. Figure 15 Specifically, a prediction model generator 210 or a prediction graph generator 212, or both, can be located at a server location 502, away from the agricultural harvester 600. Therefore, in Figure 15 In the example shown, the agricultural harvester 600 accesses the system via a remote server location 502.

[0227] Figure 15 Another example of a remote server architecture is also described. Figure 15 It shows Figure 2Some components can be located at a remote server location 502, while others can be located elsewhere. As an example, data storage device 202 can be located at a separate location from location 502 and accessed via a remote server at location 502. Regardless of their location, these components can be directly accessed by the agricultural harvester 600 via a network (such as a wide area network or local area network); these components can be hosted as a service at a remote site; or they can be 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 an operator, user, or system. For example, a physical carrier wave can be used instead of an electromagnetic carrier wave, or a physical carrier wave can be used in addition to an electromagnetic carrier wave. In some examples, where wireless telecommunications service coverage is poor or nonexistent, 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 a 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. The collected information can then be forwarded to another network when the machine containing the received information reaches a location with wireless telecommunications service coverage or other available wireless coverage. For example, when the 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 combine harvester 600 until it enters an area with wireless communication coverage. The combine harvester 600 itself can transmit the information to another network.

[0228] It will also be noted that Figure 2 The components or parts thereof can be arranged 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.

[0229] In some examples, the remote server architecture 500 may include network security measures. These measures, without limitation, include encryption of data on storage devices, encryption of data transmitted between network nodes, authentication of personnel or processes accessing data, and the use of a ledger to record 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).

[0230] Figure 16This is a simplified block diagram illustrating a schematic example of a handheld computing device or mobile computing device 16 that can be used as a user's or customer's handheld device, in which the system (or a portion thereof) can be deployed. For example, a mobile device could be deployed in the operator's compartment of an agricultural harvester 100 for use in generating, processing, or displaying the diagrams discussed above. Figures 17 to 18 Examples are handheld or mobile devices.

[0231] Figure 16 A general block diagram of the components of client device 16, which can run on client device 16, is provided. Figure 2 Some of the components shown in the diagram, the client device 16 can be connected to Figure 2 Some components shown interact, 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, a channel is provided for automatically receiving information (e.g., by scanning). Examples of communication link 13 include those allowing 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.

[0232] In other examples, applications can be received 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 represented as a processor or server from other figures), which is also connected to memory 21 and input / output (I / O) components 23, as well as clock 25 and positioning system 27.

[0233] In one example, I / O component 23 is provided to facilitate input and output operations. Various examples of I / O component 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.

[0234] Clock 25 schematically includes a real-time clock component that outputs the time and date. Schematically, clock 25 may also provide timing functionality for processor 17.

[0235] Positioning system 27 schematically includes components that output the current geographic location of device 16. Positioning system 27 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. Positioning system 27 may also include, for example, mapping or navigation software that generates desired maps, navigation routes, and other geographic functions.

[0236] 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 the function of those components.

[0237] Figure 17 The illustration shows an example where device 16 is a tablet computer 600. Figure 17 In the diagram, computer 600 is shown with a user interface display screen 602. Screen 602 may be a touchscreen or a pen-enabled interface that receives input from a pen or stylus. Tablet computer 600 may also use an on-screen virtual keyboard. Of course, computer 600 may also be attached to a keyboard or other user input device, for example, via a suitable attachment mechanism (such as a wireless link or USB port). Computer 600 may also schematically receive voice input.

[0238] Figure 18 Similar to Figure 8 In addition to being a smartphone 71, the smartphone 71 has a touch-sensitive display 73 that shows icons, tiles, or other user input mechanisms 75. Users can use these mechanisms 75 to run applications, make calls, perform data transfer operations, etc. Generally, the smartphone 71 is built on a mobile operating system and offers more advanced computing power and connectivity than feature phones.

[0239] Note that other forms of device 16 are possible.

[0240] Figure 19 It is one of the deployable ones Figure 2 An example of a computing environment for the components. Reference Figure 19 An example system for implementing some embodiments includes a computing device in the form of a computer 810 programmed to operate as discussed above. Components of 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 connects various system components, including the system memory, to the processing unit 820. 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 19 In the corresponding part.

[0241] 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 characteristics set or changed in a manner that encodes information in the signal.

[0242] 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. By way of example and not limitation, Figure 19 The operating system 834, application program 835, other program modules 836, and program data 837 are shown.

[0243] Computer 810 may also include other removable / non-removable volatile / non-volatile computer storage media. This is just one example. Figure 19A 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).

[0244] 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), systems-on-chips (SoCs), complex programmable logic devices (CPLDs), etc.

[0245] The above discussion and Figure 19 The driver and its associated computer storage media shown provide storage for computer-readable instructions, data structures, program modules, and other data for computer 810. For example, in Figure 19 In this diagram, hard disk drive 841 is shown storing operating system 844, application programs 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 programs 835, other program modules 836, and program data 837.

[0246] 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 controllers, satellite dishes, scanners, etc. These and other input devices are typically connected to processing unit 820 via user input interface 860, which is connected 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.

[0247] 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)) of one or more remote computers (such as remote computer 880).

[0248] 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 19 This demonstrates that remote application 885 can reside on remote computer 880.

[0249] It should also be noted that the different examples described in this paper 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 paper.

[0250] Example 1 is an agricultural operating machine, comprising:

[0251] A communication system that receives a map, the map including values ​​of an agricultural characteristic corresponding to different geographical locations in a field;

[0252] A geolocation sensor that detects the geolocation of the agricultural machinery;

[0253] A field sensor that detects a value for the ear of grain corresponding to the geographical location;

[0254] A prediction map generator generates a functional prediction map of the field based on the values ​​of the agricultural characteristics in the map and the values ​​of the ear size, the functional prediction map mapping the predicted control values ​​to the different geographical locations in the field;

[0255] Controllable subsystem; and

[0256] A control system that generates control signals to control the controllable subsystem based on the geographical location of the agricultural machinery and the predicted control values ​​in the functional prediction map.

[0257] Example 2 is any or all of the agricultural operating machines of the foregoing examples, wherein the prediction map generator includes:

[0258] A predictive ear specification map generator generates a functional predictive ear specification map as the functional prediction map, which maps the predicted ear specifications as the predicted control values ​​to the different geographical locations in the field.

[0259] Example 3 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system includes:

[0260] A cover plate position controller generates a cover plate position control signal based on the detected geographic location and the functional predicted ear size map, and controls the controllable subsystem based on the cover plate position control signal to control the spacing between at least one set of covers on the agricultural operation machine.

[0261] Example 4 is any or all of the agricultural operating machines of the foregoing examples, wherein the prediction map generator includes:

[0262] A predictive operator command graph generator generates a functional predictive operator command graph as the functional predictive graph, which maps predicted operator commands to the different geographical locations in the field.

[0263] Example 5 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system includes:

[0264] A controller is configured to generate an operator command control signal that indicates operator commands based on the detected geographic location and the functional predictive operator command map, and to control the controllable subsystem to execute operator commands based on the operator command control signal.

[0265] Example 6 is any or all of the agricultural operating machines of the foregoing examples, further including:

[0266] A predictive model generator generates a predictive model based on the values ​​of the agricultural characteristics in the graph at the geographical location and the values ​​of the ear size detected by the field sensors corresponding to the geographical location. The predictive model models the relationship between the agricultural characteristics and the ear size.

[0267] The prediction map generator generates the functional prediction map based on the values ​​of the agricultural characteristics in the map and based on the prediction model.

[0268] Example 7 is an agricultural operating machine of any or all of the foregoing examples, wherein the graph is a vegetation index graph, the vegetation index graph including values ​​of vegetation index characteristics as values ​​of the agricultural characteristics, and the agricultural operating machine further includes:

[0269] A prediction model generator generates a prediction model based on the values ​​of the vegetation index characteristics in the vegetation index map at the geographical location and the values ​​of the ear size detected by the field sensors corresponding to the geographical location. The prediction model models the relationship between the vegetation index characteristics and the ear size.

[0270] The prediction map generator generates the functional prediction map based on the values ​​of the vegetation index characteristics in the vegetation index map and based on the prediction model.

[0271] Example 8 is an agricultural operating machine of any or all of the previous examples, wherein the graph is a yield graph, the yield graph including values ​​of the yield characteristic as values ​​of the agricultural characteristic, and the agricultural operating machine further includes:

[0272] A predictive model generator generates a predictive model based on the yield characteristic value at the geographical location in the yield map and the ear size value corresponding to the geographical location detected by the field sensors. The predictive model models the relationship between the yield characteristic and the ear size.

[0273] The prediction map generator generates the functional prediction map based on the values ​​of the production characteristics in the production map and based on the prediction model.

[0274] Example 9 is an agricultural operating machine of any or all of the previous examples, wherein the graph is a seeding graph, the seeding graph includes values ​​of seeding characteristics as values ​​of the agricultural characteristics, and the agricultural operating machine further includes:

[0275] A prediction model generator generates a prediction model based on the values ​​of the sowing characteristics in the sowing map at the geographical location and the values ​​of the ear size detected by the field sensors corresponding to the geographical location. The prediction model models the relationship between the sowing characteristics and the ear size.

[0276] The prediction map generator generates the functional prediction map based on the values ​​of the seeding characteristics in the seeding map and based on the prediction model.

[0277] Example 10 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system further includes:

[0278] An operator interface controller generates a user interface diagram representation of the functional prediction map, the user interface diagram representation including field portions with one or more markers indicating the predicted control values ​​at one or more geographic locations on the field portions.

[0279] Example 11 is a computer-implemented method for controlling agricultural machinery, comprising:

[0280] Obtain a map, the map including values ​​of an agricultural characteristic corresponding to different geographical locations in the field;

[0281] Detect the geographical location of the agricultural machinery;

[0282] The ear size corresponding to the geographical location is detected using field sensors;

[0283] A functional prediction map of the field is generated based on the values ​​of the agricultural characteristics in the map and the values ​​of the ear size. This functional prediction map maps the predicted control values ​​to the different geographical locations within the field.

[0284] The controllable subsystem is controlled based on the geographical location of the agricultural machinery and the control values ​​in the functional prediction map.

[0285] Example 12 is a computer-implemented method of any or all of the foregoing examples, wherein generating a functional prediction graph includes:

[0286] A functional predicted ear size map is generated, which maps the predicted ear size to the different geographical locations in the field.

[0287] Example 13 is a computer-implemented method of any or all of the foregoing examples, wherein controlling the controllable subsystem includes:

[0288] A cover plate position control signal is generated based on the detected geographical location and the functional predicted ear size map; and

[0289] The controllable subsystem is controlled based on the cover plate position control signal to control the spacing between at least one set of cover plates on the agricultural machine.

[0290] Example 14 is a computer-implemented method of any or all of the foregoing examples, wherein generating a functional prediction graph includes:

[0291] Generate a functional predictive operator command map, which maps predicted operator commands to the different geographic locations in the field.

[0292] Example 15 is a computer-implemented method of any or all of the foregoing examples, wherein controlling the controllable subsystem includes:

[0293] Based on the detected geographic location and the functional predictive operator command map, an operator command control signal is generated to indicate operator commands; and

[0294] The controllable subsystem is controlled to execute the operator's command based on the operator's command control signal.

[0295] Example 16 is any or all of the agricultural operating machines of the foregoing examples, and further includes:

[0296] A prediction model is generated based on the values ​​of the agricultural characteristics shown in the figure at the geographical location and the values ​​of the ear size detected by the field sensors corresponding to the geographical location. The prediction model models the relationship between the agricultural characteristics and the ear size.

[0297] The prediction map generator generates the functional prediction map based on the values ​​of the agricultural characteristics in the map and based on the prediction model.

[0298] Example 17 is a computer-implemented method of any or all of the foregoing examples, wherein the graph is a vegetation index graph, the vegetation index graph including values ​​of vegetation index characteristics as values ​​of the agricultural characteristics, and the computer-implemented method further includes:

[0299] A prediction model is generated based on the values ​​of the vegetation index characteristics in the vegetation index map at the geographical location and the values ​​of the ear size detected by the field sensors corresponding to the geographical location. The prediction model models the relationship between the vegetation index characteristics and the ear size.

[0300] The generation of the functional prediction map includes generating the functional prediction map based on the values ​​of the vegetation index characteristics in the vegetation index map and based on the prediction model.

[0301] Example 18 is a computer-implemented method of any or all of the foregoing examples, wherein the graph is a yield graph, the yield graph including values ​​of yield characteristics as values ​​of the agricultural characteristics, and the computer-implemented method further includes:

[0302] A prediction model is generated based on the yield characteristic values ​​at the geographical location shown in the yield map and the ear size values ​​corresponding to the geographical location detected by the field sensors. This prediction model models the relationship between the yield characteristic and the ear size.

[0303] The generation of the functional prediction map includes generating the functional prediction map based on the values ​​of the production characteristics in the production map and based on the prediction model.

[0304] Example 19 is a computer-implemented method of any or all of the foregoing examples, wherein the graph is a seeding graph, the seeding graph includes values ​​of seeding characteristics as values ​​of the agricultural characteristics, and the computer-implemented method further includes:

[0305] A prediction model is generated based on the values ​​of the sowing characteristics in the sowing map at the geographical location and the values ​​of the ear size detected by the field sensors corresponding to the geographical location. The prediction model models the relationship between the sowing characteristics and the ear size.

[0306] The generation of the functional prediction map includes generating the functional prediction map based on the values ​​of the seeding characteristics in the seeding map and based on the prediction model.

[0307] Example 20 is an agricultural operating machine, comprising:

[0308] A communication system that receives a map, the map including values ​​of an agricultural characteristic corresponding to different geographical locations in a field;

[0309] A geolocation sensor that detects the geolocation of the agricultural machinery;

[0310] A field sensor that detects a value for the ear of grain corresponding to the geographical location;

[0311] A prediction model generator generates a prediction model based on the value of the agricultural characteristic in the figure at the geographical location and the value of the ear size detected by the field sensor corresponding to the geographical location. The prediction model models the relationship between the agricultural characteristic and the ear size.

[0312] A prediction map generator generates a functional prediction map of the field based on the values ​​of the agricultural characteristics in the map and based on the prediction model, the functional prediction map mapping the predicted control values ​​to the different geographical locations in the field;

[0313] Controllable subsystem; and

[0314] A control system that generates control signals to control the controllable subsystem based on the geographical location of the agricultural machinery and the control values ​​in the functional prediction map.

[0315] Although the subject matter has been described in language specific to structural features or methodological actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are disclosed as examples of the claims.

Claims

1. An agricultural operating machine (100), comprising: A communication system (206) receives a map (258) comprising values ​​of an agricultural characteristic corresponding to different geographical locations in a field; A geolocation sensor (204) detects the geolocation of the agricultural machinery (100); A field sensor (208) detects a value for an ear of grain corresponding to the geographical location; A prediction map generator (212) generates a functional predicted ear size map of the field based on the values ​​of the agricultural characteristics in the map and the values ​​of the ear size detected by the field sensors. The functional predicted ear size map maps the predicted values ​​of ear size to the different geographical locations in the field. Controllable subsystem (216); and A control system (214) generates control signals to control the controllable subsystem (216) based on the geographical location of the agricultural machine (100) and based on the predicted value of the ear size in the functional predicted ear size map.

2. The agricultural machinery according to claim 1, wherein, The control system includes: A cover plate position controller generates a cover plate position control signal based on the detected geographic location and the functional predicted ear size map, and controls the controllable subsystem based on the cover plate position control signal to control the spacing between at least one set of covers on the agricultural operation machine.

3. The agricultural machinery according to claim 1, wherein, The prediction map generator includes: A predictive operator command graph generator generates a functional predictive operator command graph that maps predicted operator commands to the different geographical locations in the field.

4. The agricultural machinery according to claim 3, wherein, The control system includes: A setting controller is configured to generate an operator command control signal that indicates an operator command based on the detected geographic location and the functional predictive operator command map, and to control the controllable subsystem to execute the operator command based on the operator command control signal.

5. The agricultural machinery according to claim 1, further comprising: A predictive model generator generates a predictive model based on the values ​​of the agricultural characteristics in the graph at the geographical location and the values ​​of the ear size detected by the field sensors corresponding to the geographical location. The predictive model models the relationship between the agricultural characteristics and the ear size. The prediction map generator generates the functional predicted ear size map based on the values ​​of the agricultural characteristics in the map and based on the prediction model. Wherein, the figure (258) is derived from a priori operations, or the data type in the figure (258) is different from the data type sensed by the field sensor.

6. The agricultural machinery according to claim 1, wherein, The graph is a vegetation index graph, which includes values ​​of the vegetation index characteristic as values ​​of the agricultural characteristic, and the agricultural machinery further includes: A prediction model generator generates a prediction model based on the values ​​of the vegetation index characteristics in the vegetation index map at the geographical location and the values ​​of the ear size detected by the field sensors corresponding to the geographical location. The prediction model models the relationship between the vegetation index characteristics and the ear size. The prediction map generator generates the functional predicted spikelet specification map based on the values ​​of the vegetation index characteristics in the vegetation index map and based on the prediction model.

7. The agricultural machinery according to claim 1, wherein, The graph is a yield graph, which includes values ​​of the yield characteristic as values ​​of the agricultural characteristic, and the agricultural operating machine further includes: A predictive model generator generates a predictive model based on the yield characteristic value at the geographical location in the yield map and the ear size value corresponding to the geographical location detected by the field sensors. The predictive model models the relationship between the yield characteristic and the ear size. The prediction map generator generates the functional predicted ear specification map based on the values ​​of the yield characteristics in the yield map and based on the prediction model.

8. A computer-implemented method for controlling agricultural machinery (100), comprising: Obtain a graph (258) that includes values ​​of an agricultural characteristic corresponding to different geographical locations in the field; Detect the geographical location of the agricultural machinery (100); The value corresponding to the geographical location of an ear of grain is detected using a field sensor (208); A functional predicted ear size map of the field is generated based on the values ​​of the agricultural characteristics in the map and the values ​​of the ear size detected by the field sensors. This functional predicted ear size map maps the predicted ear size values ​​to the different geographical locations within the field. The controllable subsystem (216) is controlled based on the geographical location of the agricultural machine (100) and based on the predicted value of the ear specification in the functional ear specification map.

9. An agricultural operating machine (100), comprising: A communication system (206) receives a map (258) comprising values ​​of an agricultural characteristic corresponding to different geographical locations in a field; A geolocation sensor (204) detects the geolocation of the agricultural machinery (100); A field sensor (208) detects a value for an ear of grain corresponding to the geographical location; A prediction model generator (210) generates a prediction model based on the value of the agricultural characteristic in the figure (258) at the geographical location and the value of the ear size detected by the field sensor corresponding to the geographical location. The prediction model models the relationship between the agricultural characteristic and the ear size. A prediction map generator (212) generates a functional predicted ear specification map of the field based on the values ​​of the agricultural characteristics in the map (258) and based on the prediction model, the functional predicted ear specification map mapping the predicted values ​​of ear specifications to the different geographical locations in the field; Controllable subsystem (216); and A control system (214) generates control signals to control the controllable subsystem (216) based on the geographical location of the agricultural machine (100) and based on the predicted value of the ear size in the functional predicted ear size map.

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