Crop composition map generator and control system

Abstract

One or more maps are obtained by an agricultural work machine. The one or more 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 that predicts a predicted agricultural property at different locations in the field based on a relationship between the values in the one or more maps and the agricultural property sensed by the on-board sensor. The prediction map can be output and used for automated machine control.

Description

TECHNICAL FIELD

[0001] This specification relates to agricultural machines, forestry machines, construction machines, and turf management machines. BACKGROUND

[0002] There are various different types of agricultural machines. Some agricultural machines include harvesters, such as combine harvesters, sugar cane harvesters, cotton harvesters, self-propelled forage harvesters, and swathers. Some harvesters can also be equipped with different types of headers to harvest different types of crops.

[0003] A wide variety of different conditions in a field can have several adverse effects on a harvesting operation. Therefore, during a harvesting operation, an operator can attempt to modify the controls of the harvester when encountering these conditions.

[0004] The above discussion is provided as background information only and is not intended to act as an aid in determining the scope of the subject matter claimed. SUMMARY

[0005] One or more maps are obtained by an agricultural work machine. The one or more maps map one or more agricultural property values at different geographic locations of a field. As the agricultural work machine moves through the field, a field sensor on the agricultural work machine senses an agricultural property. A prediction map generator generates a prediction map that predicts the agricultural property at different locations in the field based on a relationship between the values in the one or more maps and the agricultural property sensed by the field sensor. The prediction map can be output and used for automatic machine control.

[0006] The summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it used to determine or limit the scope of the claimed subject matter. The claimed subject matter is not limited to addressing any or all disadvantages in the background.

[0007] The above discussion is provided as background information only and is not intended to act as an aid in determining the scope of the subject matter claimed. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 is a partial schematic view of an example of an agricultural harvester.

[0009] Figure 2 is a block diagram showing some parts of an agricultural harvester in more detail according to some examples of the present disclosure.

[0010] Figures 3A-3B (Fig. 3, collectively referred to herein as Fig. 3) shows a flowchart illustrating an example of the operation of an agricultural harvester in generating a map.

[0011] Figure 4 is a block diagram showing one example of a prediction model generator and a prediction map generator.

[0012] Figure 5 is a flowchart or both of an agricultural harvester receiving a map, detecting field characteristics, and generating a functional prediction map to present or use to control the agricultural harvester during a harvesting operation.

[0013] Figure 6A is a block diagram showing one example of a prediction model generator and a prediction map generator.

[0014] Figure 6B is a block diagram showing some examples of field sensors.

[0015] Figure 7 shows a flowchart illustrating an example of an operation of an agricultural harvester using a priori information map and field sensor inputs to generate a functional prediction map.

[0016] Figure 8 is a block diagram showing one example of a control zone generator.

[0017] Figure 9 is a block diagram showing Figure 8 is a flowchart showing one example of an operation of the control zone generator shown.

[0018] Figure 10 shows a flowchart illustrating an example of an operation of a control system in selecting target setpoints to control an agricultural harvester.

[0019] Figure 11 is a block diagram of one example of an operator interface controller.

[0020] Figure 12 is a flowchart of one example of an operator interface controller.

[0021] Figure 13 is a diagram showing one example of an operator interface display.

[0022] Figure 14 is a block diagram showing one example of an agricultural harvester in communication with a remote server environment.

[0023] Figures 15-17 shows an example of a mobile device that can be used in an agricultural harvester.

[0024] Figure 18 is a block diagram showing one example of a computing environment that can be used in an agricultural harvester. DETAILED DESCRIPTION

[0025] To facilitate an understanding of the principles of the present disclosure, reference will now be made to the examples illustrated in the drawings, and specific language will be used to describe them. It will, nevertheless, be understood that no limitation of the scope of the disclosure is intended. Any alterations and further modifications in the described devices, systems, methods, and any further applications of the principles of the present disclosure are fully contemplated as are of a person having ordinary skill in the art to which the present disclosure pertains. Particularly, it is fully contemplated that features, components, steps, or combinations thereof described with respect to one example can be incorporated into other examples of the present disclosure as well.

[0026] The present specification relates to using in-field data acquired contemporaneously with an agricultural operation in combination with prior data to generate functional prediction maps, and more specifically, functional prediction crop composition maps. In some examples, the functional prediction crop composition maps can be used to control an agricultural work machine (e.g., an agricultural harvester). It can be desirable to vary or otherwise control machine settings of the agricultural harvester according to crop composition values in the area in which the agricultural harvester is operating. In certain situations, high crop composition values (e.g., protein or oil) can result in premium market prices or yields when feeding livestock. To obtain this value, crops are separated and managed according to composition levels at harvest. Separation can occur by directing the crops to one of a plurality of on-board clean grain tanks. In other examples, separation can occur by unloading grain to a grain transport vehicle when a composition level threshold is exceeded. In other examples, separation can occur by managing a path of the harvesting vehicle through the field based on the predicted composition values.

[0027] In one example, the systems herein can obtain an agricultural property map that maps values of one or more agricultural properties to different locations in one or more fields of interest. Thus, the agricultural property map provides georeferenced agricultural property values that indicate values of one or more agricultural properties at different locations in the field of interest. Agricultural properties can include any property that can affect an agricultural operation. For example, but not limited to, agricultural properties can include crop or vegetation (e.g., weed) properties of the field, soil properties, terrain properties, machine properties of an agricultural work machine (e.g., machine settings, operating properties, or machine performance properties), such as machine speed or power utilization, yield, biomass, seeding properties (seed genotype, seed population, seed spacing, and various other seeding properties), crop state, compaction, detected agricultural properties during previous operations, and any of a number of agricultural properties. Some examples of particular agricultural property maps will now be described.

[0028] The vegetation index map illustratively maps vegetation index values at different geographic locations in one or more fields of interest, which can be indicative of vegetation growth. One example of a vegetation index includes the normalized difference vegetation index (NDVI). There are many other vegetation indices as well, and all are within the scope of the present disclosure. In some examples, the vegetation index 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.

[0029] The vegetation index map can thus be used to identify the presence and location of vegetation. In some examples, the vegetation index map enables identification and georegistration of crops in the presence of bare soil, crop residue, or other plant presence including crops or weeds. For example, at the beginning of the growing season, when crops are in a growing state, the vegetation index can show the progress of crop development. Thus, if the vegetation index map is generated early in the growing season or midway through the growing season, the vegetation index map can indicate the development progress of the crop plants. For example, the vegetation index map can indicate whether the plants are underdeveloped, whether sufficient canopy has been established, or other plant properties of plant development.

[0030] Based on data collected during operations prior to the agricultural harvesting operation, the a priori operations map maps values of various a priori operation characteristics to different locations in one or more fields of interest. Prior operations by different machines can have an impact on the operation of the agricultural harvester. For example, prior spraying or substance application operations can have an impact on or otherwise share a relationship with the final composition in the crops on the field to be harvested. For example, application of fertilizer (e.g., nitrogen-containing fertilizer), application of herbicides, pesticides, fungicides, and various other substances can have an impact on or share a relationship with the final composition of the crops, including the composition of the grain in the crops. For example, application of fertilizer (e.g., nitrogen-containing fertilizer) can have an impact on or otherwise share a relationship with the final composition of the crops on the field, including the composition of the grain in the crops. Thus, the a priori operations map provides georegistered a priori operation characteristics that are agricultural characteristics based on data collected during prior operations. In one example, the a priori operations map can include georegistered substance (e.g., fertilizer) application values that are indicative of substance application characteristics, such as the location, quantity, type, and composition of substances applied by agricultural work machines (e.g., sprayers, seeders, spreaders, and various other substance application machines).

[0031] Historical crop composition maps illustratively map crop composition values for different geographic locations in one or more fields of interest. These historical crop composition maps are collected from past agricultural operations (e.g., past harvesting operations) on one or more fields. The crop composition maps can display crop composition in crop composition value units. One example of a crop composition value unit includes a numerical value, such as a percentage, a weight value, or a mass value, that indicates an amount of a composition in a crop, such as an amount of protein, starch, oil, nutrients, water, and various other compositions in a crop or vegetation, or in grain of a crop plant, or an amount of protein, starch, oil, nutrients, water, and various other compositions in grain of a crop plant. Some crop compositions are more transient in nature, as the amount of a composition contained in crop material (e.g., grain) changes over time, for example, as grain dries or absorbs water over time. Some crop compositions are more structural in nature, as the amount of a composition (or a ratio of compositions) tends to not change as much over time, at least until the grain decomposes. As used herein, crop composition can also refer to grain composition, and thus in some examples, a crop composition value can refer to an amount of a composition in grain of a crop plant, such as an amount of protein, starch, oil, nutrients, water, and various other compositions in grain of a crop plant. In some examples, historical crop composition maps can be derived from sensor readings of one or more crop composition sensors. Without limitation, these crop composition sensors can utilize one or more bands of electromagnetic radiation to detect crop composition. For example, a crop composition sensor can utilize reflection or absorption of electromagnetic radiation of various ranges (e.g., various wavelengths or frequencies, or both) by crop or other vegetation material to detect crop composition. In some examples, a crop composition sensor can include an optical sensor, such as a spectrometer. In one example, a crop composition sensor can utilize near-infrared spectroscopy or visible and near-infrared spectroscopy.

[0032] Soil property maps illustratively map soil property values (which can indicate soil type, soil moisture, soil coverage, soil structure, and various other soil properties) throughout different geographic locations in one or more fields of interest. Thus, soil property maps provide georeferenced soil properties throughout a field of interest. Soil type can refer to a taxonomic unit in soil science, where each soil type includes a defined set of shared characteristics. Soil types can include, for example, sandy soil, clay soil, silt soil, peat soil, chalk soil, loam soil, and various other soil types. Soil moisture can refer to the amount of water held or otherwise contained in soil. Soil moisture can also be referred to as soil wetness. Soil coverage can refer to the amount of an item or material covering soil, including vegetation material, such as crop residue or cover crop, debris, and various other items or materials. Generally, in agricultural terms, soil coverage includes a measure of remaining crop residue (e.g., a remaining amount of plant stalks) and a measure of cover crop. Soil structure can refer to the arrangement of solid portions of soil and the interstitial spacing between the solid portions of soil. Soil structure can include the manner in which individual particles (such as individual particles of sand, silt, and clay) are combined. Soil structure can be described in terms of grade (degree of aggregation), class (average size of aggregates), and form (type of aggregates), among various other descriptions. Soil composition can include a number of soil composition characteristics, such as the location, quantity, and type of constituents in soil, such as nutrients and other plant nutrients. In one example, soil composition can include values indicative of the quantity, location, and type of soil composition in a field of interest, such as a soil composition map can be a nitrogen map indicative of the quantity of nitrogen in soil present at different locations in a field of interest. These are merely examples. Various other characteristics and properties of soil can be mapped on soil property maps as soil property values.

[0033] In some examples, the system can receive a predicted characteristic map. The predicted characteristic map illustratively maps predicted characteristic values throughout one or more geographic locations in one or more fields of interest. For example, the predicted characteristic map can be a predicted biomass map that maps predicted values of biomass or biomass characteristics (e.g., vegetation height, vegetation density, vegetation mass, vegetation volume, threshing cylinder drive force) throughout one or more geographic locations in one or more fields of interest. In another example, the predicted characteristic map can be a predicted yield map that maps predicted yield values throughout one or more geographic locations in one or more fields of interest. In another example, the predicted characteristic map can be a predicted crop moisture map that maps predicted values of crop moisture throughout one or more geographic locations in one or more fields of interest.

[0034] Accordingly, the present discussion is directed to examples in which the system receives one or more agricultural property maps, such as, but not limited to, one or more of a vegetation index map, one or more prediction maps (e.g., a predicted yield map, a predicted moisture map, or a predicted biomass map), a prior operation map (e.g., a prior operation map showing a material use property), a historical crop composition map of the field, or a soil property map (e.g., a nitrogen map), and the system also uses in-field sensors to detect a property or variable indicative of a crop composition property during a harvesting operation. These are merely examples of some of the maps that can be received. Various other agricultural property maps are also contemplated herein. The system generates a model that models relationships between agricultural property values from one or more of the received maps and from in-field sensor data, vegetation index values, predicted property values (e.g., predicted biomass values, predicted yield values, or predicted crop moisture values), prior operation property values (e.g., material application property values), historical crop composition values, or soil property values (e.g., soil composition values (e.g., nitrogen values)). The model is used to generate a functional predicted crop composition map that predicts crop composition values in the field. The functional predicted crop composition map generated during the harvesting operation can be presented to an operator or other user, or used to automatically control the agricultural harvester during the harvesting operation, or both.

[0035] Figure 1 is a partial schematic view of a self-propelled agricultural harvester 100. In the example shown, the agricultural harvester 100 is a combine harvester. Further, although a combine harvester is provided as an example throughout this disclosure, it will be understood that the present description also applies to other types of harvesters, such as a cotton harvester, a sugarcane harvester, a self-propelled forage harvester, a swather, or other agricultural work machines. Accordingly, the present disclosure is intended to encompass the various different types of harvesters described, and is therefore not limited to a combine harvester. Further, the present disclosure relates to other types of work machines, such as agricultural planters and sprayers, construction equipment, forestry equipment, and turf management equipment, to which the generation of prediction maps can be applicable. Accordingly, the present disclosure is intended to encompass these various different types of harvesters and other work machines, and is therefore not limited to a combine harvester.

[0036] As Figure 1As shown, the agricultural harvester 100 illustratively includes an operator’s room 101 that can have a variety of different operator interface mechanisms for controlling the agricultural harvester 100. The agricultural harvester 100 includes a front end apparatus, such as a header 102 and a cutter 104 generally indicated. The agricultural harvester 100 also includes a feeder housing 106, a feeder accelerator 108, and a threshing machine generally indicated at 110. The feeder housing 106 and the feeder accelerator 108 form part of a material handling subsystem 125. The header 102 is pivotably coupled to a frame 103 of the agricultural harvester 100 along a pivot axis 105. One or more actuators 107 drive the header 102 to move about the axis 105 in a direction generally indicated by arrow 109. Thus, the vertical position of the header 102 above the ground 111 on which the header 102 travels (header height) can be controlled by actuating the actuators 107. Although not shown in the Figure 1 The agricultural harvester 100 can also include one or more actuators that operate to impart a tilt angle, a roll angle, or both, to the header 102 or portions of the header 102. Tilt refers to the angle at which the cutter 104 engages the crop. For example, the tilt angle is increased by controlling the header 102 to cause the distal edge 113 of the cutter 104 to point more toward the ground. The tilt angle is decreased by controlling the header 102 to cause the distal edge 113 of the cutter 104 to point more away from the ground. Roll angle refers to the orientation of the header 102 about a fore-aft longitudinal axis of the agricultural harvester 100.

[0037] The threshing machine 110 illustratively includes a threshing cylinder 112 and a set of concaves 114. In addition, the agricultural harvester 100 also includes a separator 116. The agricultural harvester 100 also includes a cleaning subsystem or cleaning house 118 (collectively, cleaning subsystem 118) that includes a cleaning fan 120, a chaffer 122, and a sieve 124. The material handling subsystem 125 also includes an unloading beater 126, a residue elevator 128, a clean grain elevator 130, and an unloading auger 134 and a spout 136. The clean grain elevator moves clean grain into a clean grain tank 132. The agricultural harvester 100 also includes a residue subsystem 138 that can include a chopper 140 and a spreader 142. The agricultural harvester 100 also includes a propulsion subsystem that includes an engine that drives ground engaging assemblies 144 (e.g., wheels or tracks). In some examples, a combine within the scope of the present disclosure can have more than one of any of the subsystems described above. In some examples, the agricultural harvester 100 can have left and right cleaning subsystems, separators, etc., not shown in FIG. 1. Figure 1 In some examples, the agricultural harvester 100 can have left and right cleaning subsystems, separators, etc., not shown in FIG. 1.

[0038] In operation, as outlined, the agricultural harvester 100 is illustratively moved through a 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 issued by the operator. The operator of the agricultural harvester 100 can determine one or more of a height setting, a tilt angle setting, or a roll angle setting of the header 102. For example, the operator inputs one or more settings to a control system (described in greater detail below) that controls the actuators 107. The control system can also receive settings from the operator to establish the tilt angle and roll angle of the header 102 and implement the input settings by controlling the associated actuators (not shown) that change the tilt angle and roll angle of the header 102. The actuators 107 maintain the header 102 at a height above the ground 111 based on the height setting, and, where applicable, at a desired tilt angle and roll angle. Each of the height setting, roll setting, and tilt setting can be implemented independent of the other settings. The control system responds to header errors (e.g., a difference between the height setting and a measured height of the header 104 above the ground 111, and, in some cases, tilt angle errors and roll angle errors) with a responsiveness determined based on a selected sensitivity level. If the sensitivity level is set at a greater sensitivity level, the control system responds to smaller header position errors and attempts to reduce detected errors more quickly than when the sensitivity is at a lower sensitivity level.

[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 moves through the feeder housing 106 by a conveyor toward the feed accelerator 108, which accelerates the crop material into the threshing machine 110. The crop is threshed by rotating the cylinder 112 against the concave 114. The threshed crop is moved by the separator cylinder in the separator 116, with a portion of the residue moved toward the residue subsystem 138 by the discharge beater 126. The portion of the residue that is conveyed to the residue subsystem 138 is chopped by the residue chopper 140 and spread on the field by the spreader 142. In other configurations, the residue is discharged from the agricultural harvester 100 in a pile. In other examples, the residue subsystem 138 can include a grass seed rejector (not shown), such as a seed bagger or other seed collector or a seed pulverizer or other seed breaker.

[0040] The grain falls into the cleaning subsystem 118. The chaffer 122 separates some larger material from the grain, and the sieve 124 separates some fine material from the clean grain. The clean grain falls onto a screw conveyor that moves the grain to the inlet end of the clean grain elevator 130, and the clean grain elevator 130 moves the clean grain upward, depositing the clean grain in the clean grain bin 132. The airflow generated by the cleaning fan 120 removes the residue from the cleaning subsystem 118. The cleaning fan 120 directs air up through the sieve and the chaffer along an airflow path. The airflow transports the residue back in the agricultural harvester 100 toward the residue handling subsystem 138.

[0041] The residue elevator 128 returns the residue to the threshing machine 110, where the residue is re-threshed. Alternatively, the residue can also be delivered by the residue elevator or another transport device to a separate re-threshing mechanism, where the residue is also re-threshed.

[0042] Figure 1 Also shown, in one example, the agricultural harvester 100 includes a ground speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward-looking image capture mechanism 151 (which can be in the form of a stereo camera or a monocular camera), and one or more loss sensors 152 disposed in the cleaning subsystem 118.

[0043] The ground speed sensor 146 senses the speed of travel of the agricultural harvester 100 over the ground. The ground speed sensor 146 can sense the speed of travel 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 such as a global positioning system (GPS), a dead reckoning system, a LORAN system, or a variety of different other systems or sensors that provide an indication of the speed of travel can be used.

[0044] The loss sensors 152 illustratively provide output signals indicative of the amount of grain loss occurring in both the right and left sides of the cleaning subsystem 118. In some examples, the sensors 152 are impact sensors that count the number of grain impacts per unit of time or per unit of travel distance to provide an indication of the grain loss occurring at the cleaning subsystem 118. The impact sensors on the right and left sides of the cleaning subsystem 118 can provide separate signals or a combined or aggregated signal. In some examples, rather than providing separate sensors for each cleaning subsystem 118, the sensors 152 can include a single sensor.

[0045] The separator loss sensors 148 provide an indication of the amount of grain loss occurring in the left and right separators 114, 116. In some examples, the sensors 148 are impact sensors that count the number of grain impacts per unit of time or per unit of travel distance to provide an indication of the grain loss occurring in the left and right separators 114, 116. Figure 1The separator loss sensors 148 can be associated with the left and right separators and can provide separate grain loss signals or a combined or aggregate signal. In some cases, various different types of sensors can also be used to sense grain loss in the separators.

[0046] The agricultural harvester 100 can also include other sensors and measurement mechanisms. For example, the agricultural harvester 100 can include one or more of the following sensors: a header height sensor that senses a height of the header 102 above the ground 111; a stability sensor that senses oscillation or bounce (and amplitude) of the agricultural harvester 100; a residue setting sensor configured to sense whether the agricultural harvester 100 is configured to chop residue, swath, or the like; a clean grain elevator fan speed sensor that senses a speed of the clean grain elevator fan 120; a concave gap sensor that senses a gap between the cylinder 112 and the concave 114; a threshing cylinder speed sensor that senses a cylinder speed of the cylinder 112; a chaffer gap sensor that senses an opening size in the chaffer 122; a screen gap sensor that senses an opening size in the screen 124; a material other than grain (MOG) moisture sensor (e.g., a capacitive moisture sensor) that senses a moisture level of the MOG passing through the agricultural harvester 100; one or more machine setting sensors configured to sense a variety of configurable settings of the agricultural harvester 100; a machine orientation sensor that senses an orientation of the agricultural harvester 100; and a crop property sensor that senses various different types of crop properties, such as crop type, crop moisture, crop constituent properties, and other crop properties. The crop property sensor can also be configured to sense properties of cut crop material as the agricultural harvester 100 is processing the crop material. For example, in some cases, the crop property sensor can sense: grain mass, such as broken grain, MOG levels; grain constituents, such as starch and protein; and grain feed rate as the grain passes through the feeder housing 106, the clean grain elevator 130, or elsewhere in the agricultural harvester 100. The crop property sensor can also sense a biomass feed rate through the feeder housing 106, the separator 116, or elsewhere in the agricultural harvester 100. The crop property sensor can also sense a mass flow rate of grain through the elevator 130 or through other portions of the agricultural harvester 100, or provide other output signals indicative of other sensed variables. The crop property sensor can include one or more crop constituent sensors that sense properties indicative of crop constituents of the crop, including properties of constituents of grain of the crop.

[0047] Without limitation, the crop composition sensor can utilize one or more bands of electromagnetic radiation to detect crop composition. For example, the crop composition sensor can utilize the reflection or absorption of electromagnetic radiation of various ranges (e.g., various wavelengths or frequencies, or both) by crop or other vegetation material, including grain, to detect crop composition. In some examples, the crop composition sensor can include an optical sensor, such as a spectrometer. In one example, the crop composition sensor can utilize near-infrared spectroscopy or visible near-infrared spectroscopy. The crop composition sensor can be disposed at or have access to various locations within the agricultural harvester 100. For example, the crop composition sensor can be disposed within (or otherwise have sensing access to) the feeder housing 106 and configured to detect the composition of harvested crop material passing through the feeder housing 106. In other examples, the crop composition sensor can be located at other areas within the agricultural harvester 100, such as on or connected to the clean grain elevator, in the clean grain auger, or in the grain tank. In some examples, the crop composition sensor can include a capacitive sensor, which can include, for example, a capacitor for determining the dielectric properties of crop (and other vegetation) material, such as the dielectric properties of grain. It should be noted that these are merely examples of types and locations of crop composition sensors, and various other types and locations of crop composition sensors are contemplated.

[0048] Before describing how the agricultural harvester 100 generates and uses a functional predicted crop composition map, a brief description of some items on the agricultural harvester 100 and their operation will first be described. Figure 2 , Figure 3A and Figure 3BThe drawings of the'378 application describe receiving a general type of prior information map and combining information from the prior information map with georegistered sensor signals generated by in-field sensors, where the sensor signals are indicative of characteristics in a field, such as characteristics of crops or weeds present in the field. Characteristics of the field can include, but are not limited to, characteristics of the field, such as slope, weed density, weed type, soil moisture, surface quality; characteristics of crop properties, such as crop height, crop composition, crop density, crop status; characteristics of grain properties, such as grain moisture, grain size, grain test weight, grain composition; and characteristics of machine performance, such as loss level, work quality, fuel consumption, and power utilization. Relationships between characteristic values obtained from the in-field sensor signals and the prior information map values are identified, and the relationships are used to generate a new functional prediction map. The functional prediction map predicts values at different geographic locations in the field, and one or more of those values can be used to control a machine, such as one or more subsystems of an agricultural harvester. In some cases, the functional prediction map can be presented to a user, such as an operator of an agricultural work machine (which can be an agricultural harvester). The functional prediction map can be presented to the user visually (e.g., via a display), haptically, or aurally. The user can interact with the functional prediction map to perform editing operations and other user interface operations. In some cases, the functional prediction map can be used to control an agricultural work machine (e.g., an agricultural harvester), presented to an operator or other user, and presented to an operator or user to facilitate one or more of operator or user interaction.

[0049] After describing general methods with reference to Figure 2 , Figure 3A and Figure 3B , more specific methods of generating a functional prediction crop composition map that can be presented to an operator or user or used to control an agricultural harvester 100, or both, are described with reference to Figure 4 and Figure 5 . Again, although this discussion is directed to an agricultural harvester (specifically, a combine harvester), the scope of the present disclosure encompasses other types of agricultural harvesters or other agricultural work machines.

[0050] Figure 2 is a block diagram showing some portions of an example agricultural harvester 100. Figure 2The agricultural harvester 100 is shown to illustratively include one or more processors or servers 201, data storage 202, a geo-location sensor 204, a communication system 206, and one or more field sensors 208 that sense one or more agricultural characteristics of the field contemporaneously with the harvesting operation. Agricultural characteristics can include any characteristic that can have an impact on the harvesting operation. Some examples of agricultural characteristics include characteristics of the harvesting machine, the field, the plants on the field, and the weather. Other types of agricultural characteristics are also included. The field sensors 208 generate values corresponding to the sensed characteristics. The agricultural harvester 100 also includes a predictive model or relationship generator (hereinafter collectively referred to as "predictive model generator 210"), a predictive map generator 212, a control zone generator 213, a control system 214, one or more controllable subsystems 216, and an operator interface mechanism 218. The agricultural harvester 100 can also include a variety of different other agricultural harvester functions 220. For example, the field sensors 208 include on-board sensors 222, remote sensors 224, and other sensors 226 that sense characteristics of the field during the course of the agricultural operation. The predictive model generator 210 illustratively includes a priori information variable to field variable model generator 228, and the predictive model generator 210 can include other items 230. The control system 214 includes a communication system controller 229, an operator interface controller 231, a settings controller 232, a path planning controller 234, a feed rate controller 236, a header and reel controller 238, a belt conveyor belt controller 240, a cover position controller 242, a residue system controller 244, a machine cleanout controller 245, a zone controller 247, and the control system 214 can include other items 246. The controllable subsystems 216 include machine and header actuators 248, a propulsion subsystem 250, a steering subsystem 252, a residue subsystem 138, a machine cleanout subsystem 254, and the controllable subsystems 216 can include a variety of different other subsystems 256.

[0051] Figure 2It is also shown that the agricultural harvester 100 can receive one or more prior information maps 258. As described below, the one or more prior information maps 258 include, for example, agricultural property maps, such as a vegetation index map, a prior operation map, a historical crop composition map, or a soil property map. However, the one or more prior information maps 258 can also encompass other types of data obtained prior to the harvesting operation or maps from prior or previous operations, such as historical crop composition data obtained prior to the harvesting operation or historical crop composition maps containing situational information associated with the historical crop composition properties for the past several years. The situational information can include, but is not limited to, one or more weather conditions for the growing season, the presence of pests, the geographic location, soil properties including soil composition (e.g., nitrogen levels), irrigation, material applications, and the like. The weather conditions can include, but are not limited to, precipitation for the entire season, the presence of gales, temperature for the entire season, and the like. Some examples of pests broadly include insects, fungi, weeds, bacteria, viruses, and the like. Some examples of material applications include herbicides, pesticides, fungicides, fertilizers, mineral supplements, and the like. Figure 2 It is also shown that an operator 260 can operate the agricultural harvester 100. The operator 260 interacts with the operator interface mechanism 218. In some examples, the operator interface mechanism 218 can include joysticks, joypads, steering wheels, linkages, pedals, buttons, dials, keypads, user-actuatable elements on a user interface display device (e.g., icons, buttons, and the like), microphones and speakers (where voice recognition and speech synthesis are provided), and a variety of different other types of control devices. Where a touch-sensitive display system is provided, the operator 260 can utilize touch gestures to interact with the operator interface mechanism 218. The above examples are provided as illustrative examples and are not intended to limit the scope of the present disclosure. Thus, other types of operator interface mechanisms 218 can also be used and are within the scope of the present disclosure.

[0052] Using the communication system 206 or otherwise, the prior information maps 258 can be downloaded onto the agricultural harvester 100 and stored in the data storage device 202. In some examples, the communication system 206 can be a cellular communication system, a system that communicates via a wide area network or a local area network, a system that communicates via a near field communication network, or a communication system configured to communicate via any of a variety of different other networks or a combination of networks. The communication system 206 can also include a system that facilitates the downloading or transfer of information to and from a secure digital (SD) card or a universal serial bus (USB) card or both.

[0053] The geographic position sensor 204 illustratively senses or detects a geographic position or location of the agricultural harvester 100. The geographic position sensor 204 can include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from GNSS satellite transmitters. The geographic position sensor 204 can also include a real-time kinematic (RTK) component configured to enhance the accuracy of position data derived from GNSS signals. The geographic position sensor 204 can include a dead reckoning system, a cellular triangulation system, or any of a variety of different other geographic position sensors.

[0054] The field sensors 208 can be any of the sensors described above with reference to Figure 1 The field sensors 208 include on-board sensors 222 mounted on the agricultural harvester 100. For example, these sensors can include impact plate sensors, radiation attenuation sensors, or image sensors inside the agricultural harvester 100 (e.g., a clean grain camera). The field sensors 208 can also include remote field sensors 224 that capture field information. Field data includes data acquired from sensors on the agricultural harvester or acquired by any sensor in the case of detecting data during a harvesting operation. Some other examples of field sensors are shown in Figure 6B

[0055] After being retrieved by the agricultural harvester 100, the prior information graph selector 209 can filter or select one or more particular prior information graphs 258 for use by the predictive model generator 210. In one example, the prior information graph selector 209 selects one or more particular prior information graphs 258 based on a comparison of situational information in the prior information graphs to current situational information. For example, a historical crop composition graph can be selected from a year in the past several years that has similar characteristics of material application to the current year's material application characteristics. Or, for example, a historical crop composition graph can be selected from a year in the past several years when the situational information is dissimilar. For example, a historical crop composition graph can be selected for a year before a "dry" (i.e., dry conditions or reduced precipitation) year and the current year is a "wet" (i.e., increased precipitation or flood conditions) year. There can still be a useful historical relationship, but the relationship can be inverse. For example, areas in the field that have low levels of one or more crop compositions in a dry year can be areas of higher crop composition in a wet year because these areas can provide better growing conditions in wet years. The current situational information can include situational information beyond the immediate situation information. For example, the current situational 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 before the current growing season, or a set of data corresponding to the past several years, etc.

[0056] ​Context information can also be used for correlation between areas with similar context characteristics, regardless of whether the geographic locations correspond to the same locations on the prior information map 258. For example, historical crop composition values from areas with similar agricultural characteristics (e.g., vegetation index values, material application characteristics, soil properties (e.g., soil composition characteristics), and various other agricultural characteristics) can be used as the prior information map 258 to create a predicted crop characteristics map. For example, context characteristics information associated with different locations can be applied to locations on the prior information map 258 with similar characteristics information.

[0057] The predictive model generator 210 generates models that indicate relationships between values sensed by the field sensors 208 and characteristics mapped to the field by the prior information map 258. For example, if the prior information map 258 maps agricultural characteristic values to different locations in the field and the field sensors 208 are sensing values indicative of crop composition, then the prior information variable to field variable model generator 228 generates a predictive crop composition model that models the relationship between the agricultural characteristic values and the crop composition values. The predictive map generator 212 then uses the predictive crop composition model generated by the predictive model generator 210 to generate a functional predictive crop composition map that predicts values of crop composition at different locations in the field based on the prior information map 258. Or for example, if the prior information map 258 maps vegetation index values to different locations in the field and the field sensors 208 are sensing values indicative of crop composition, then the prior information variable to field variable model generator 228 generates a predictive crop composition model that models the relationship between the vegetation index values and the crop composition values. The predictive map generator 212 then uses the predictive crop composition model generated by the predictive model generator 210 to generate a functional predictive crop composition map that predicts values of crop composition at different locations in the field based on the prior information map 258. Or for example, if the prior information map 258 maps historical crop composition values to different locations in the field and the field sensors 208 are sensing values indicative of crop composition, then the prior information variable to field variable model generator 228 generates a predictive crop composition model that models the relationship between the historical crop composition values (with or without scenario information) and the field crop composition values. The predictive map generator 212 then uses the predictive crop composition model generated by the predictive model generator 210 to generate a functional predictive crop composition map that predicts values of crop composition at different locations in the field based on the prior information map 258. Or for example, if the prior information map 258 maps prior operational characteristic values (e.g., material use characteristics) to different locations in the field and the field sensors 208 are sensing values indicative of crop composition, then the prior information variable to field variable model generator 228 generates a predictive crop composition model that models the relationship between the prior operational characteristic values and the crop composition values. The predictive map generator 212 then uses the predictive crop composition model generated by the predictive model generator 210 to generate a functional predictive crop composition map that predicts values of crop composition at different locations in the field based on the prior information map 258.Or, for example, if the prior information map 258 maps soil property values (e.g., soil composition values (e.g., nitrogen values)) to different locations in the field, and the field sensors 208 are sensing values indicative of crop composition, then the prior information variable to field variable model generator 228 generates a predictive crop composition model that models the relationship between the soil property values (e.g., soil composition values) and the crop composition values. The predictive map generator 212 then uses the predictive crop composition model generated by the predictive model generator 210 to generate a functional predictive crop composition map that predicts values of crop composition at different locations in the field based on the prior information map 258.

[0058] In some examples, the type of data in the functional predictive map 263 can be the same as the type of field data sensed by the field sensors 208. In some cases, the type of data in the functional predictive map 263 can have different units than the data sensed by the field sensors 208. In some examples, the type of data in the functional predictive map 263 can be different than the type of data sensed by the field sensors 208, but related to the type of data sensed by the field sensors 208. For example, in some examples, the type of data sensed by the field sensors 208 can be indicative of the type of data in the functional predictive map 263. In some examples, the type of data in the functional predictive map 263 can be different than the type of data in the prior information map 258. In some cases, the type of data in the functional predictive map 263 can have different units than the data in the prior information map 258. In some examples, the type of data in the functional predictive map 263 can be different than the type of data in the prior information map 258, but related to the type of data in the prior information map 258. For example, in some examples, the type of data in the prior information map 258 can be indicative of the type of data in the functional predictive map 263. In some examples, the type of data in the functional predictive map 263 is different than one or both of the type of field data sensed by the field sensors 208 and the type of data in the prior information map 258. In some examples, the type of data in the functional predictive map 263 is the same as one or both of the type of field data sensed by the field sensors 208 and the type of data in the prior information map 258. In some examples, the type of data in the functional predictive map 263 is the same as one of the type of field data sensed by the field sensors 208 or the type of data in the prior information map 258, and different than the other.

[0059] Continuing the foregoing example, the predictive map generator 212 can use the values in the prior information map 258 and the models generated by the predictive model generator 210 to generate a functional predictive map 263 that predicts crop composition at different locations in the field. The predictive map generator 212 thus outputs a predictive map 264.

[0060] like Figure 2 As shown, prediction map 264 is based on prior information values ​​in prior information map 258 at those locations (or locations with type scenario information, even in different fields) and uses a prediction model to predict the values ​​of characteristics (which may be the same as those sensed by one or more field sensors 208) or the values ​​of characteristics related to those sensed by one or more field sensors 208 at multiple locations across the field. For example, if prediction model generator 210 has generated a prediction model indicating the relationship between agricultural characteristic values ​​and crop component values, then given agricultural characteristic values ​​at different locations across the field, prediction map generator 212 generates prediction map 264 predicting crop component values ​​at different locations throughout the field. Prediction map 264 is generated using the agricultural characteristic values ​​at those locations obtained from prior information map 258 and the relationship between agricultural characteristic values ​​and crop component values ​​obtained from the prediction model.

[0061] Alternatively, for example, if the prediction model generator 210 has already generated a prediction model indicating the relationship between vegetation index values ​​and crop component values, then, given the vegetation index values ​​at different locations on the field, the prediction map generator 212 generates a prediction map 264 predicting the crop component values ​​at those different locations on the field. The vegetation index values ​​at those locations obtained from the prior information map 258 and the relationship between the vegetation index values ​​and crop component values ​​obtained from the prediction model are used to generate the prediction map 264.

[0062] Or, for example, if the prediction model generator 210 has already generated a prediction model indicating the relationship between historical crop component values ​​and field-detected crop component values, then, given historical crop component values ​​at different locations in the field, the prediction map generator 212 generates a prediction map 264 predicting the values ​​of crop components at those different locations in the field. The historical crop component values ​​at those locations obtained from the prior information map 258 and the relationship between the historical crop component values ​​obtained from the prediction model and the field crop component values ​​are used to generate the prediction map 264.

[0063] Alternatively, for example, if the prediction model generator 210 has already generated a prediction model indicating the relationship between prior operational characteristic values ​​(e.g., material application characteristic values) and crop composition values, then, given prior operational characteristic values ​​at different locations in the field, the prediction map generator 212 generates a prediction map 264 predicting the crop composition values ​​at those different locations in the field. The prior operational characteristic values ​​at those locations obtained from the prior information map 258, and the relationship between the prior operational characteristic values ​​and crop composition values ​​obtained from the prediction model, are used to generate the prediction map 264.

[0064] Or, for example, if the prediction model generator 210 has generated a prediction model that indicates a relationship between soil property values (e.g., soil composition values (e.g., nitrogen level values)) and crop composition values, then, given soil property values (e.g., soil composition values) at different locations on the field, the prediction map generator 212 generates a prediction map 264 that predicts crop composition values at different locations on the field. The soil property values (e.g., soil composition values) at those locations obtained from the prior information map 258 and the relationship between the soil property values (e.g., soil composition values) and the crop composition values obtained from the prediction model are used to generate the prediction map 264.

[0065] Some variations in the types of data mapped in the prior information map 258, the types of data sensed by the field sensors 208, and the types of data predicted on the prediction map 264 will now be described.

[0066] In some examples, the type of data in the prior information map 258 is different from the type of data sensed by the field sensors 208, and the type of data in the prediction map 264 is the same as the type of data sensed by the field sensors 208. For example, the prior information map 258 can be a vegetation index map, and the variable sensed by the field sensors 208 can be crop composition. The prediction map 264 can then be a predicted crop composition map that maps predicted crop composition values to different geographic locations in the field. The prediction map 264

[0067] Additionally, in some examples, the type of data in the prior information map 258 is different from the type of data sensed by the field sensors 208, and the type of data in the prediction map 264 is different from both the type of data in the prior information map 258 and the type of data sensed by the field sensors 208. For example, the prior information map 258 can be a vegetation index map, and the variable sensed by the field sensors 208 can be crop composition. The prediction map 264 can then be a predicted biomass map that maps predicted biomass values to different geographic locations in the field. In another example, the prior information map 258 can be a vegetation index map, and the variable sensed by the field sensors 208 can be crop composition. The prediction map 264 can then be a predicted routing map that maps a predicted harvester travel path along the field.

[0068] In some examples, the prior information map 258 comes from a prior or previous pass through the field during a prior or previous operation, and the data type is different from the data type sensed by the in-field sensors 208, while the data type in the prediction map 264 is the same as the data type sensed by the in-field sensors 208. For example, the prior information map 258 can be a material application map (e.g., a fertilizer application map) generated during a material application, and the variable sensed by the in-field sensors 208 can be a crop composition characteristic. As such, the prediction map 264 can be a predicted crop composition map mapping predicted crop composition values to different geographic locations in the field. The prediction map 264

[0069] In some examples, the prior information map 258 comes from a prior or previous pass through the field during a prior or previous operation, and the data type is the same as the data type sensed by the in-field sensors 208, and the data type in the prediction map 264 is also the same as the data type sensed by the in-field sensors 208. For example, the prior information map 258 can be a crop composition map generated in a previous year, and the variable sensed by the in-field sensors 208 can be crop composition. As such, the prediction map 264 can be a predicted crop composition map mapping predicted crop composition values to different geographic locations in the field. In this example, the prediction model generator 210 can use relative crop composition differences in the georegistered prior information map 258 from the previous year to generate a prediction model modeling a relationship between relative crop composition differences on the prior information map 258 and crop composition values sensed by the in-field sensors 208 during a current harvesting operation. The prediction map generator 210 then uses the prediction model 212 to generate a predicted crop composition map.

[0070] In some examples, the prediction map 264 can be provided to a control zone generator 213. The control zone generator 213 groups adjacent portions of a zone based on data values associated with the adjacent portions of the zone in the prediction map 264 into one or more control zones. A control zone can include two or more contiguous portions of a zone (e.g., a field) for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant. For example, a response time to change a setting of a controllable subsystem 216 can not be sufficient to respond to a change in a value contained in a map, such as the prediction map 264, in a satisfactory manner. In this case, the control zone generator 213 parses the map and identifies control zones having a defined size to accommodate the response time of the controllable subsystem 216. In another example, control zones can be sized to reduce wear caused by excessive actuator movement resulting from continuous adjustments. In some examples, there can be different sets of control zones for individual controllable subsystems 216 or groups of controllable subsystems 216. Control zones can be added to the prediction map 264 to obtain a prediction control zone map 265. Thus, the prediction control zone map 265 can be similar to the prediction map 264 except that the prediction control zone map 265 includes control zone information defining control zones. Thus, a functional prediction map 263 can or can not include control zones as described herein. Both the prediction map 264 and the prediction control zone map 265 are functional prediction maps 263. In one example, the functional prediction map 263 does not include control zones (e.g., the prediction map 264). In another example, the functional prediction map 263 does include control zones (e.g., the prediction control zone map 265). In some examples, if an intercrop production system is implemented, multiple crops can be present in a field at the same time. In this case, the prediction map generator 212 and the control zone generator 213 are able to identify the location and characteristics of two or more crops and then generate the prediction map 264 and the prediction control zone map 265 accordingly.

[0071] It will also be appreciated that the control zone generator 213 can cluster values to generate control zones and that the control zones can be added to the prediction control zone map 265 or to a separate map that only displays the generated control zones. In some examples, the control zones can be used to control and / or calibrate the agricultural harvester 100. In other examples, the control zones can be presented to the operator 260 and used to control or calibrate the agricultural harvester 100, and in other examples, the control zones can be presented to the operator 260 or another user or stored for later use.

[0072] The prediction map 264 or the prediction control zone map 265 or both are provided to the control system 214, which generates control signals based on the prediction map 264 or the prediction control zone map 265 or both. In some examples, the communication system controller 229 controls the communication system 206 to communicate the prediction map 264 or the prediction control zone map 265 or control signals based on the prediction map 264 or the prediction control zone map 265 to other agricultural harvester machines that are harvesting in the same field. In some examples, the communication system controller 229 controls the communication system 206 to transmit the prediction map 264, the prediction control zone map 265 or both to other remote systems.

[0073] The operator interface controller 231 is operable to generate control signals to control the operator interface mechanisms 218. The operator interface controller 231 is also operable to present the prediction map 264 or the prediction control zone map 265 or other information derived from or based on the prediction map 264, the prediction control zone 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 mechanisms to display one or both of the prediction map 264 and the prediction control zone map 265 to the operator 260. The controller 231 can generate operator-actuatable mechanisms that are displayed and can be actuated by the operator to interact with the displayed map. The operator can edit the map by, for example, correcting crop component values displayed on the map based on the operator’s observations. The setting controller 232 can generate control signals to control a variety of settings on the agricultural harvester 100 based on the prediction map 264, the prediction control zone map 265, or both. For example, the setting controller 232 can generate control signals to control the machine and header actuators 248. In response to the generated control signals, the machine and header actuators 248 operate to control, for example, one or more of the sieve and chaffer settings, concave gap, cylinder settings, clean fan speed settings, header height, header functions, reel speed, reel position, belt conveyor functions where the agricultural harvester 100 is coupled to a belt conveyor header, grain header functions, in-bin distribution control, and other actuators 248 that affect other functions of the agricultural harvester 100. The path planning controller 234 illustratively generates control signals to control the steering subsystem 252 to steer the agricultural harvester 100 according to a desired path. The path planning controller 234 can control the path planning system to generate a route for the agricultural harvester 100 and can control the propulsion subsystem 250 and the steering subsystem 252 to steer the agricultural harvester 100 along the route. The feed rate controller 236 can control a variety of different subsystems, such as the propulsion subsystem 250 and the machine actuators 248, to control the feed rate based on the prediction map 264 or the prediction control zone map 265 or both. For example, as the agricultural harvester 100 approaches an area with high-protein wheat plants indicated by one or more crop component values, the control system 214 can adjust the speed of the clean fan 120 to perform enhanced cleaning of the high-protein wheat plant material. The header and reel controller 238 can generate control signals to control the header or reel or other header functions. The belt conveyor belt controller 240 can generate control signals to control the belt conveyor belt or other belt conveyor functions based on the prediction map 264, the prediction control zone map 265, or both.The header position controller 242 can generate control signals to control the position of the header included on the agricultural harvester 100 based on the prediction map 264 or the prediction control zone map 265 or both, and the residue system controller 244 can generate control signals to control the residue subsystem 138 based on the prediction map 264 or the prediction control zone map 265 or both. The machine cleanout controller 245 can generate control signals to control the machine cleanout subsystem 254. For example, based on the different types of seeds and weeds passing through the agricultural harvester 100, a particular type of machine cleanout operation or frequency of performing a cleanout operation can be controlled. Other controllers included on the agricultural harvester 100 can also control other subsystems based on the prediction map 264 or the prediction control zone map 265 or both.

[0074] Figure 3A and Figure 3B A flowchart is shown that illustrates one example of the operation of the agricultural harvester 100 in generating the prediction map 264 and the prediction control zone map 265 based on the prior information map 258.

[0075] At block 280, the agricultural harvester 100 receives the prior information map 258. Examples of the prior information map 258 or receiving the prior information map 258 are discussed with reference to blocks 281, 282, 284, and 286. As discussed above, the prior information map 258 maps values of a variable corresponding to a first characteristic to different locations in a field, as indicated by block 282. For example, one prior information map can be a prior operation map generated during a prior operation, or based on data from a prior operation on the field, such as a previous material application operation by a material application machine, such as a spray operation by an agricultural sprayer or a spread operation by an agricultural spreader. In another example, the prior information map can be a historical crop composition map based on data from a prior harvesting operation. In another example, the prior information map can be a vegetation index map. In another example, the prior information map can be a soil composition map. These are merely examples. The prior information map can include any of a plurality of agricultural characteristic maps that map values of an agricultural characteristic to different geographic locations in one or more fields of interest. The data for the prior information map 258 can also be collected in other ways. For example, the data can be collected based on aerial images or measurements taken in a previous year, or earlier than the current growing season, or at other times. This information can also be based on data detected or collected in other ways besides using aerial images. For example, the data for the prior information map 258 can be transmitted to the agricultural harvester 100 using the communication system 206 and stored in the data storage 202. The data for the prior information map 258 can be provided to the agricultural harvester 100 in other ways using the communication system 206, and this is indicated by block 286 in the flowchart. Figure 2 The prior information map 258 can be received through the communication system 206 in some examples.

[0076] At block 287, the prior information map selector 209 can select one or more maps from the plurality of candidate prior information maps received in block 280. For example, historical crop composition maps for multiple years can be received as candidate prior information maps. Each of these maps can contain contextual information, such as weather patterns for a period of time (e.g., a year), pest outbreaks for a period of time (e.g., a year), soil properties, topographical characteristics, material application characteristics, etc. The contextual information can be used to select which historical crop composition map should be selected. For example, weather conditions for a period of time (e.g., weather conditions for the current year) or soil properties of the current field can be compared to weather conditions and soil properties in the contextual information of each candidate prior information map. The results of this comparison can be used to select which historical crop composition map should be selected. For example, years with similar weather conditions can generally result in similar crop composition characteristics or crop composition trends across the field. In some cases, years with opposite weather conditions can also be helpful in predicting crop composition from historical crop composition. For example, areas with low crop composition values in dry years can have high crop composition values in wet years because the areas can provide better growing conditions. The process of selecting one or more prior information maps by the prior information map selector 209 can be manual, semi-automatic, or automatic. In some examples, the prior information map selector 209 can continuously or intermittently determine whether different prior information maps have a better relationship with the on-site sensor values during a harvesting operation. If a different prior information map is more closely related to the on-site data, the prior information map selector 209 can replace the currently selected prior information map with the more related prior information map.

[0077] At the beginning of a harvesting operation, the on-site sensors 208 generate sensor signals indicative of one or more on-site data values indicative of a characteristic such as a crop characteristic, as indicated by block 288. Examples of on-site sensors 288 are discussed with reference to blocks 222, 290, and 226. As explained above, the on-site sensors 208 include: an on-board sensor 222; a remote on-site sensor 224, such as a UAV-based sensor that flies once to collect on-site data (shown in block 290); or other types of on-site sensors specified by the on-site sensors 226. In some examples, data from the on-board sensors is geo-registered using position, heading, or velocity data from the geo-location sensors 204.

[0078] The prediction model generator 210 controls the prior information variable-to- field variable model generator 228 to generate a model that models the relationship between the mapped values included in the prior information map 258 and the field values sensed by the field sensors 208, as indicated by block 292. The characteristics or data types represented by the mapped values in the prior information map 258 and the field values sensed by the field sensors 208 can be the same characteristics or data types, or different characteristics or data types.

[0079] The relationship or model generated by the prediction model generator 210 is provided to the prediction map generator 212. The prediction map generator 212 uses the prediction model and the prior information map 258 to generate a prediction map 264 that predicts the values of the characteristics sensed by the field sensors 208, or different characteristics related to the characteristics sensed by the field sensors 208, at different geographic locations in the field being harvested, as indicated by block 294.

[0080] It should be noted that, in some examples, the prior information map 258 can include two or more different maps, or two or more different layers of a single map. Each layer can represent a different data type than the data type of another layer, or the layers can have the same data type obtained at different times. Each of the two or more different maps, or each of the two or more different layers of a map, maps different types of variables to geographic locations in the field. In such examples, the prediction model generator 210 generates prediction models that model the relationship between the field data and each of the different variables mapped by the two or more different maps or two or more different layers of a map. Similarly, the field sensors 208 can include two or more sensors that each sense a different type of variable. Thus, the prediction model generator 210 generates prediction models that model the relationship between each type of variable mapped by the prior information map 258 and each type of variable sensed by the field sensors 208. The prediction map generator 212 can use the prediction models and each of the maps or layers of the prior information map 258 to generate a functional prediction map 263 that predicts the values of each sensed characteristic (or characteristics related to the sensed characteristics) sensed by the field sensors 208 at different locations in the field being harvested.

[0081] The prediction map generator 212 configures the prediction map 264 such that the prediction map 264 can be manipulated (or used) by the control system 214. The prediction map generator 212 can provide the prediction map 264 to the control system 214 or the control zone generator 213 or both. Some examples of different ways in which the prediction map 264 can be configured or output are described with reference to blocks 296, 295, 299, and 297. For example, the prediction map generator 212 configures the prediction map 264 such that the prediction map 264 includes values that can be read by the control system 214 and used as a basis for generating control signals for one or more different controllable subsystems of the agricultural harvester 100, as indicated by block 296.

[0082] The control zone generator 213 can divide the prediction map 264 into control zones based on the values on the prediction map 264. Geographically contiguous values within a threshold of each other can be grouped into a control zone. The threshold can be a default threshold, or can be set based on operator input, based on input from an automation system, or based on other criteria. The size of the zones can be based on the responsiveness of the control system 214, controllable subsystems 216, or based on wear considerations or other criteria, as indicated by block 295. The prediction map generator 212 configures the prediction map 264 for presentation to an operator or other user. The control zone generator 213 can configure the prediction control zone map 265 for presentation to an operator or other user. This is indicated by block 299. When presented to an operator or other user, the presentation of the prediction map 264 or the prediction control zone map 265 or both can include the predicted values on the prediction map 264 related to geographic locations, the control zones on the prediction control zone map 265 related to geographic locations, and one or more of the set values or control parameters used based on the values on the prediction map 264 or the zones on the prediction control zone map 265. In another example, the presentation can include more abstract information or more detailed information. The presentation can also include a confidence that indicates how accurate the predicted values on the prediction map 264 or the zones on the prediction control zone map 265 are to the measured values that can be measured by sensors on the agricultural harvester 100 as the agricultural harvester 100 moves through the field. Furthermore, in cases where the information is presented to more than one location, a verification and authorization system can be provided to implement a verification / authorization process. For example, there can be a hierarchy of individuals authorized to view and change the information of the maps and other presentations. As an example, an onboard display device can display the maps approximately in real-time locally on the machine, or can also generate the maps at one or more remote locations. In some examples, each physical display device at each location can be associated with a human or user permission level. The user permission level can be used to determine which display indicia are visible on the physical display device, and which values the corresponding human can change. As an example, a local operator of the agricultural harvester 100 can not be able to see the information corresponding to the prediction map 264 or make any changes to the machine operation. However, a supervisor at a remote location can be able to see the prediction map 264 on a display, but not make any changes. A manager that can be at a separate remote location can be able to see all of the elements on the prediction map 264, and also change the prediction map 264 used in the machine control. This is one example of an authorization hierarchy that can be implemented. The prediction map 264 or the prediction control zone map 265 or both can also be configured in other ways, as indicated by block 297.

[0083] At block 298, inputs from the geo-location sensor 204 and other field sensors 208 are received by the control system. Block 300 represents the control system 214 receiving input from the geo-location sensor 204 identifying the geo-location of the agricultural harvester 100. Block 302 represents the control system 214 receiving sensor input indicative of the trajectory or heading of the agricultural harvester 100, and block 304 represents the control system 214 receiving the speed of the agricultural harvester 100. Block 306 represents the control system 214 receiving other information from the variety of field sensors 208.

[0084] At block 308, the control system 214 generates control signals to control the controllable subsystems 216 based on the prediction map 264 or the prediction control zone map 265 or both, and the inputs from the geo-location sensor 204 and any other field sensors 208. At block 310, the control system 214 applies the control signals to the controllable subsystems. It will be understood that the particular control signals generated and the particular controllable subsystems 216 being controlled can vary based on one or more different things. For example, the control signals generated and the controllable subsystems 216 being controlled can be based on the type of prediction map 264 or prediction control zone map 265 or both being used. Similarly, the control signals generated, the controllable subsystems 216 being controlled, and the timing of the control signals can be based on the various delays of the crop stream through the agricultural harvester 100 and the responsiveness of the controllable subsystems 216.

[0085] As an example, the generated prediction map 264 in the form of a predicted crop composition map can be used to control one or more controllable subsystems 216. For example, a functional predicted crop composition map can include crop composition values that are geographically registered to locations within the field being harvested. The functional predicted crop composition map can be extracted and used to control the steering subsystem 252 and the propulsion subsystem 250, respectively. By controlling the steering subsystem 252 and the propulsion subsystem 250, the rate of material or grain movement through the agricultural harvester 100 can be controlled. Similarly, the header height can be controlled to ingest more or less material and, thus, the header height can also be controlled to control the rate of material movement through the agricultural harvester 100. In other examples, if the prediction map 264 maps crop composition values that are higher in front of the machine 100 over one portion of the header than another portion of the header, resulting in different biomass entering one side of the header than the other side of the header, control of the header can be implemented. For example, the belt conveyor speed on one side of the header can be increased or decreased relative to the belt conveyor speed on the other side of the header to account for the additional biomass. Thus, the header and reel controller 238 can be controlled using the geographically registered values present in the predicted crop composition map to control the belt conveyor speed of the belt conveyor belts on the header. The foregoing examples involving rate of material movement and header control using a functional predicted crop composition map are provided by way of example only. Thus, a variety of other control signals can be generated using values obtained from a predicted crop composition map or other types of functional predicted maps to control one or more controllable subsystems 216.

[0086] At block 312, a determination is made as to whether the harvesting operation has been completed. If harvesting has not been completed, the process proceeds to block 314 in which field sensor data from the geo-location sensors 204 and the field sensors 208 (and possibly other sensors) is continually read.

[0087] In some examples, at block 316, the agricultural harvester 100 can also detect learning trigger criteria to perform machine learning on one or more of the prediction map 264, the predicted control zone map 265, the models generated by the prediction model generator 210, the zones generated by the control zone generator 213, one or more control algorithms implemented by the controllers in the control system 214, and other trigger-based learning.

[0088] The learning trigger criteria can include any of a variety of different criteria. Some examples of detection trigger criteria are discussed with respect to blocks 318, 320, 321, 322, and 324. For example, in some examples, trigger-based learning can involve recreating the relationships used to generate the prediction model when a threshold amount of field sensor data is obtained from the field sensors 208. In these examples, receiving more than a threshold amount of field sensor data from the field sensors 208 triggers or causes the prediction model generator 210 to generate a new prediction model used by the prediction map generator 212. Thus, as the agricultural harvester 100 continues the harvesting operation, receiving a threshold amount of field sensor data from the field sensors 208 triggers the creation of new relationships represented by the prediction models generated by the prediction model generator 210. Further, new prediction maps 264, prediction control zone maps 265, or both can be regenerated using the new prediction models. Block 318 represents detecting a threshold amount of field sensor data for triggering the creation of a new prediction model.

[0089] In other examples, the learning trigger criteria can be based on how much the field sensor data from the field sensors 208 has changed, e.g., over time or compared to previous or prior values. For example, if the change within 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, or less than a defined amount, or below a threshold, the prediction model generator 210 does not generate a new prediction model. As a result, the prediction map generator 212 does not generate new prediction maps 264 and / or prediction control zone maps 265. However, if the change within the field sensor data is outside of a selected range, greater than a defined amount, or above a threshold, for example, the prediction model generator 210 generates a new prediction model using all or a portion of the newly received field sensor data used by the prediction map generator 212 to generate new prediction maps 264. At block 320, the change in the field sensor data (e.g., the magnitude of the amount of data outside of the 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 a trigger for causing the generation of a new prediction model and prediction map. Continuing with the example described above, the threshold, range, and defined amount can be set to a default value, set by an operator or user through user interface interaction, set by the automated system, or otherwise set.

[0090] Other learning trigger criteria can also be used. For example, if the prediction model generator 210 switches to a different prior information map (different than the initially selected prior information map 258), the switching to the different prior information map can trigger the prediction model generator 210, the prediction map generator 212, the control zone generator 213, the control system 214, or other items to relearn. In another example, the agricultural harvester 100 transitioning to a different terrain or a different control zone can also be used as a learning trigger criteria.

[0091] In some cases, the operator 260 can also edit the prediction map 264 or the prediction control zone map 265 or both. Such editing can change values on the prediction map 264 and / or change the size, shape, location or existence of control zones on the prediction control zone map 265. Block 321 shows that the edited information can be used as a learning trigger criterion.

[0092] In some cases, the operator 260 can also observe that the automatic control of the controllable subsystem is not as desired by the operator. In these cases, the operator 260 can provide manual adjustments to the controllable subsystem, which reflect that the operator 260 desires the controllable subsystem to operate differently than commanded by the control system 214. Accordingly, the manual changes to the settings by the operator 260 can cause one or more of the following to be performed based on the adjustments made by the operator 260, such as shown in block 322: causing the prediction model generator 210 to relearn the model, causing the prediction map generator 212 to regenerate the map 264, causing the control zone generator 213 to regenerate one or more control zones on the prediction control zone map 265, and causing the control system 214 to relearn its control algorithm or perform machine learning on one or more of the controller components 232-246 in the control system 214. Block 324 represents the use of other trigger-based learning criteria.

[0093] In other examples, the relearning can be performed periodically or intermittently based on, for example, a selected time interval (e.g., a discrete time interval or a variable time interval), as indicated by block 326.

[0094] If the relearning is triggered (whether based on a learning trigger criterion or based on the passage of a time interval), as indicated by block 326, one or more of the prediction model generator 210, the prediction map generator 212, the control zone generator 213, and the control system 214 perform machine learning to generate new prediction models, new prediction maps, new control zones, and new control algorithms, respectively, based on the learning trigger criterion. The new prediction models, new prediction maps, and new control algorithms are generated using any additional data collected since the last learning operation was performed. The performance of the relearning is indicated by block 328.

[0095] If the harvesting operation has been completed, the operation moves from block 312 to block 330, in which one or more of the prediction map 264, the prediction control zone map 265, and the prediction model generated by the prediction model generator 210 are stored. The prediction map 264, the prediction control zone map 265, and the prediction model can be stored locally on the data storage device 202 or can be transmitted to a remote system using the communication system 206 for subsequent use.

[0096] It is noted that while some examples herein describe the prediction model generator 210 and the prediction map generator 212 receiving a prior information map when generating a prediction model and a functional prediction map, respectively, in other examples, the prediction model generator 210 and the prediction map generator 212 can receive other types of maps when generating a prediction model and a functional prediction map, respectively, including a prediction map, such as a functional prediction map generated during a harvesting operation. For example, the prediction model generator 210 and the prediction map generator 212 can receive a prediction map, such as a predicted biomass map, a predicted yield map, or a predicted crop moisture map.

[0097] Figure 4 is a block diagram of a portion of the agricultural harvester 100 shown in Figure 1 Particularly, in addition to other items, Figure 4 Examples of the prediction model generator 210 and the prediction map generator 212 are shown in more detail. Figure 4Information flow between the various different components shown therein is also illustrated. As shown, the prediction model generator 210 receives one or more of a vegetation index map 332, a historical crop composition map 333, a prior operations map 341, a soil properties map 343, a prediction map 353 (e.g., a predicted biomass map, a predicted crop moisture map, or a predicted yield map), or an agricultural characteristics map 347. In some examples, the prediction map (e.g., a predicted biomass map, a predicted yield map, or a predicted crop moisture map) is generated by the processes described in FIG. 3. In other examples, the prediction model generator 210 can receive various other maps 401. The historical crop composition map 333 includes historical crop composition values 335 that indicate crop composition values for the entire field during the last harvest. The historical crop composition map 333 also includes scenario data 337 that indicates backgrounds or conditions that can have influenced the crop composition values over the past year or years. For example, the scenario data 337 can include soil properties (e.g., soil type, soil moisture, soil coverage, or soil structure), soil composition (e.g., nitrogen content), topographical characteristics (e.g., elevation or slope), planting date, harvest date, material application (e.g., fertilizer application) characteristics, seed genotype (hybrid, breed, etc.), measure of weed presence, measure of pest presence, weather conditions (e.g., rainfall, snowfall, hail, wind, temperature), etc. The historical crop composition map 333 can also include other items, as shown by block 339. As shown in the example shown, the vegetation index map 332, the prior operations map 341, the soil composition map 343, and the agricultural characteristics map 347 do not contain additional information. However, in other examples, the vegetation index map 332, the prior operations map 341, the soil composition map 343, the prediction map 353, and the agricultural characteristics map 347 can also include other items. For example, weed growth has an influence on vegetation index readings. Thus, herbicide applications that are temporally related to the vegetation index sensing used to generate the vegetation index map 332 can be scenario information included in the vegetation index map 332 to provide context for the vegetation index values. Various other types of information can also be included in the vegetation index map 332, the topographical map 341, or the soil properties map 343.

[0098] In addition to receiving one or more of the vegetation index map 332, the historical crop composition map 333, the prior operations map 341, the soil composition map 343, the prediction map 353, or the agricultural characteristics map 347 as prior information maps, the prediction model generator 210 also receives a geographic location 334 or an indication of a geographic location from the geographic location sensor 204. The on-site sensors 208 illustratively include a crop composition sensor 336 and a processing system 338. The processing system 338 processes sensor data generated by the crop composition sensor 336. In some examples, the crop composition sensor 336 can be on-board the agricultural harvester 100.

[0099] In some examples, the crop composition sensor 336 can utilize one or more bands of electromagnetic radiation to detect crop composition. For example, the crop composition sensor 336 can utilize the reflection or absorption of various ranges (e.g., various wavelengths or frequencies, or both) of electromagnetic radiation by crop or other vegetation material, including grain of the crop plants, to detect crop composition. In some examples, the crop composition sensor can include an optical sensor, such as a spectrometer. In one example, the crop composition sensor 336 can utilize near-infrared spectroscopy or visible and near-infrared spectroscopy. The crop composition sensor 336 can be disposed at or have access to various locations within the agricultural harvester 100. For example, the crop composition sensor 336 can be disposed within the feeder housing 106 (or otherwise have sensing access to crop material within the feeder housing 106) and configured to detect the composition of harvested crop material passing through the feeder housing 106. In other examples, the crop composition sensor 336 can be located at other areas within the agricultural harvester 100, such as located in or coupled to or disposed in a clean grain elevator, located in or coupled to or disposed in a clean grain auger, or located in or coupled to or disposed in a grain tank. It should be noted that these are merely examples of types and locations of the crop composition sensor 336, and various other types and locations of the crop composition sensor 336 are contemplated.

[0100] The processing system 338 processes one or more sensor signals generated by the crop composition sensor 336 to generate processed sensor data that determines one or more crop composition values. The processing system 338 can also geolocate values received from the field sensors 208. For example, the location of the agricultural harvester when receiving signals from the field sensors 208 can not be the precise location of the crop composition values. This is because a period of time can pass between the agricultural harvester initially contacting a crop plant and the crop composition sensor 336 or other field sensors 208 sensing the crop plant. Thus, when georeferencing the sensed data, the transition time between initially encountering the plant and the plant material being sensed within the agricultural harvester is considered. By doing so, the crop composition values can be precisely georeferenced to the exact location on the field. For example, because the severed crop travels in a direction transverse to the direction of travel of the agricultural harvester along the header, the crop composition values can be geolocated to the scissor-shaped region behind the agricultural harvester as the agricultural harvester travels forward.

[0101] The processing system 338 assigns or allocates the total crop composition values detected by the crop composition sensor during each time or measurement interval back to the earlier georeferenced areas based on the travel time of the crop from different portions of the agricultural harvester (e.g., different lateral positions along the width of the header of the agricultural harvester) and the ground speed of the harvester. For example, the processing system 338 assigns the measured total crop composition values from a measurement interval or time back to the georeferenced areas traversed by the header of the agricultural harvester during the different measurement intervals or times. The processing system 338 assigns or allocates the total crop composition values from a particular measurement interval or time to previously traversed georeferenced areas that are part of the herringbone areas.

[0102] In some examples, the crop composition sensor 336 can rely on different types of radiation and the way that the radiation is reflected, absorbed, attenuated, or transmitted by the crop material including the grain. The crop composition sensor 336 can sense other electromagnetic properties of the crop material, such as the dielectric constant, as the material passes between two capacitive plates. Other material properties and sensors can also be used. In some examples, the raw data or processed data from the crop composition sensor 336 can be presented to the operator 260 through the operator interface mechanism 218. The operator 260 can be located on the work agricultural harvester 100 or at a remote location.

[0103] The present discussion is directed to an example where the crop composition sensor 336 detects crop composition values. It should be understood that this is merely an example and that the above-described sensors are also contemplated herein as other examples of the crop composition sensor 336. As Figure 4 As shown, the prediction model generator 210 includes a vegetation index to crop composition model generator 342, a historical crop composition to crop composition model generator 344, a soil composition to crop composition model generator 345, a prior operational characteristic to crop composition model generator 346, a predicted characteristic to crop composition model generator 347, and an agricultural characteristic to crop composition model generator 348. In other examples, the prediction model generator 210 can include more or fewer model generators than those shown. Figure 4The components shown in the examples of FIGS. 1-3 are more, fewer, or different components in some examples. Thus, in some examples, the prediction model generator 210 can also include other items 349, which can include other types of prediction model generators for generating other types of crop constituent models. For example, the other model generators 348 can include specific characteristics, such as specific vegetation index characteristics (e.g., crop growth or crop health), specific soil constituent characteristics (e.g., nitrogen levels), specific prior operational characteristics (e.g., substance application characteristics from prior substance application operations), specific agricultural characteristics, specific prediction characteristics (e.g., predicted biomass, predicted yield, predicted crop moisture), or specific crop constituents (e.g., oil, starch, protein, and various other crop constituents).

[0104] The vegetation index to crop constituent model generator 342 determines a relationship between the in-field crop constituent data 340 at a geographic location corresponding to the location at which the in-field crop constituent data 340 is geolocated and the vegetation index values from the vegetation index map 332 corresponding to the same location in the field at which the in-field crop constituent data 340 is geolocated. Based on this relationship established by the vegetation index to crop constituent model generator 342, the vegetation index to crop constituent model generator 342 generates a prediction crop constituent model. The prediction map generator 212 uses the prediction crop constituent model to predict crop constituent values at different locations in the field based on the georeferenced vegetation index values contained in the vegetation index map 332 at the same geographic locations in the field.

[0105] The historical crop constituent to crop constituent model generator 344 determines a relationship between the crop constituents at a geographic location corresponding to the location at which the in-field crop constituent data 340 is geolocated represented in the in-field crop constituent data 340 and historical crop constituents at the same location (or a location in the historical crop constituent map 333 having similar scenario data as the current region or year). The historical crop constituent values 335 are values contained in the historical crop constituent map 333 that are georeferenced and referenced in terms of scenario. The historical crop constituent to crop constituent model generator 344 then generates a prediction crop constituent model based on the historical crop constituent values 335 that the prediction map generator 212 uses to predict crop constituents at locations in the field.

[0106] The soil property to crop composition model generator 345 determines a relationship between the in-field crop composition data 340 at a geographic location corresponding to a location where the in-field crop composition data 340 is geolocated and soil property values (e.g., soil composition values, soil type values, soil moisture values, etc.) from the soil property map 343 corresponding to the same location in the field where the in-field crop composition data 340 is geolocated. Based on this relationship established by the soil property to crop composition model generator 345, the soil property to crop composition model generator 345 generates a predictive crop composition model. The predictive map generator 212 uses the predictive crop composition model to predict crop composition at different locations in the field based on georeferenced soil property values at the same locations in the field contained in the soil property map 343.

[0107] The prior operational characteristic to crop composition model generator 346 determines a relationship between the in-field crop composition data 340 at a geographic location corresponding to a location where the in-field crop composition data 340 is geolocated and prior operational characteristic values (e.g., material application characteristic values) from the prior map 341 corresponding to the same location in the field where the in-field crop composition data 340 is geolocated. Based on this relationship established by the prior operational characteristic to crop composition model generator 346, the prior operational characteristic to crop composition model generator 346 generates a predictive crop composition model. The predictive map generator 212 uses the predictive crop composition model to predict crop composition at different locations in the field based on georeferenced prior operational characteristic values at the same locations in the field contained in the prior operational map 341.

[0108] The predictive characteristic to crop composition model generator 347 identifies a relationship between the in-field crop composition data 340 at a geographic location corresponding to a location where the in-field crop composition data 340 is geolocated and predictive characteristic values (e.g., predictive biomass values, predictive yield values, or predictive crop moisture values) from the predictive map 353 corresponding to the same location in the field where the in-field crop composition data 340 is geolocated. Based on this relationship established by the predictive characteristic to crop composition model generator 347, the predictive characteristic to crop composition model generator 347 generates a predictive crop composition model. The predictive map generator 212 uses the predictive crop composition model to predict crop composition at different locations in the field based on georeferenced predictive characteristic values (e.g., georeferenced predictive biomass values, georeferenced predictive yield values, or georeferenced predictive crop moisture values) at the same locations in the field contained in the predictive map 353.

[0109] The agricultural trait-to-crop composition model generator 348 identifies the relationship between the field crop composition data 340 at a geographic location corresponding to the location where the field crop composition data 340 is geolocated, and the agricultural trait values ​​from the agricultural trait map 347 corresponding to the same geographic location where the field crop composition data 340 is geolocated in the field. Based on this relationship established by the agricultural trait-to-crop composition model generator 348, the agricultural trait-to-crop composition model generator 348 generates a predictive crop composition model. The prediction map generator 212 uses the predictive crop composition model to predict the crop composition at different locations in the field based on the georegistered agricultural trait values ​​contained in the agricultural trait map 347 at the same location in the field.

[0110] In view of the above, the prediction model generator 210 is operable to generate multiple prediction crop composition models, such as one or more of the prediction crop composition models generated by model generators 342, 344, 345, 346, 348, 357, and 349. In another example, two or more of the above-described prediction crop composition models can be combined into a single prediction crop composition model, which predicts crop composition values ​​based on vegetation index values, historical crop composition values, soil property values, prior operational characteristic values, prediction characteristic values, or agricultural characteristic values ​​at different locations in the field. Any one or a combination of these crop composition models is generated by... Figure 4 The crop component model 350 in the model is represented by the same component.

[0111] The crop composition prediction model 350 is provided to the prediction map generator 212. Figure 4 In one example, the prediction map generator 212 includes a crop composition map generator 352. In other examples, the prediction map generator 212 may include additional, fewer, or different map generators. The crop composition map generator 352 receives a prediction crop composition model 350, which predicts crop composition based on field data 340 and one or more of the following: a vegetation index map 332, a historical crop composition map 333, a priori operational map 341, a soil property map 343, a prediction map 353, or an agricultural characteristic map 347.

[0112] The crop composition map generator 352 can generate a functional predicted crop composition map 360 that predicts crop composition at different locations in a field based on one or more of vegetation index values at the different locations in the field, historical crop composition values, prior operational characteristic values, soil property values (e.g., soil composition values), predicted characteristic values (e.g., predicted yield values, predicted biomass values, or predicted crop moisture values), or agricultural characteristic values. Crop composition values can include amounts of one or more components in crop material, such as amounts of protein, oil, starch, or other components in crop material (e.g., crop plants). In other examples, crop composition values can include amounts of one or more components in grain of crop plants, such as amounts of protein, oil, starch, or other components in grain (e.g., corn kernels, soybeans, etc.). The generated functional predicted crop composition map 360 can be provided to the control zone generator 213, the control system 214, or both. The control zone generator 213 generates control zones and incorporates these control zones into the functional predicted map, i.e., the predicted map 360, to produce a predicted control zone map 265. One or both of the functional predicted map 264 or the predicted control zone map 265 can be presented to an operator 260 or other user, or provided to the control system 214, which generates control signals to control one or more controllable subsystems 216 based on the predicted map 264, the predicted control zone map 265, or both.

[0113] Figure 5 is a flowchart of an example of operations of the prediction model generator 210 and the prediction map generator 212 in generating a predicted crop composition model 350 and a functional predicted crop composition map 360. At block 362, the prediction model generator 210 and the prediction map generator 212 receive one or more of the prior vegetation index maps 332, one or more of the historical crop composition maps 333, one or more of the prior operational maps 341, one or more of the soil composition maps 343, one or more of the agricultural characteristic maps 347, or combinations thereof. At block 362, field sensor signals are received from field sensors, such as crop composition sensor signals from the crop composition sensors 336.

[0114] At block 363, the prior information map selector 209 selects a prior information map 250 of one or more characteristics for use by the predictive model generator 210. In one example, the prior information map selector 209 selects a map from a plurality of candidate maps based on a comparison of situational information of the candidate maps to current situational information. For example, a candidate historical crop composition map can be selected from a previous year that had weather conditions of the growing season similar to the current year. Or, for example, a candidate historical crop composition map can be selected from a previous year that had lower than average precipitation levels, even though the current year has average or above average precipitation levels, because the historical crop composition map associated with the previous year that had lower average precipitation levels can still have useful historical crop composition to crop composition relationships, as described above. In some examples, the prior information map selector 209 can change the prior information map being used upon detecting that one of the other candidate prior information maps is more closely related to the crop composition sensed in the field.

[0115] At block 372, the processing system 338 processes one or more received sensor signals received from the field sensors 208 (e.g., one or more sensor signals received from the crop composition sensor 336) to generate an amount of one or more components present in the harvested crop material, such as an amount of protein, an amount of starch, or an amount of oil, among various other crop components. Other field sensors 208 can also be used, as indicated by block 370. Some examples of other field sensors are shown in Figure 6B

[0116] At block 382, the predictive model generator 210 also obtains a geographic location corresponding to the sensor signals. For example, the predictive model generator 210 can obtain a geographic location from the geographic location sensor 204 and determine an accurate geographic location to which the field sensed crop composition is attributed based on machine latency (e.g., machine processing speed) and machine speed. For example, the exact time at which a crop composition sensor signal is captured can not correspond to the geographic location of the crop composition value. Thus, the location of the agricultural harvester 100 when the crop composition sensor signal is obtained can not correspond to the location at which the crop having the crop composition was planted.

[0117] ​At block 384, the prediction model generator 210 generates one or more predictive crop composition models, e.g., the crop composition model 350, that model a relationship between at least one of the vegetation index values obtained from the map, the historical crop composition values, the prior operational characteristic values, the soil property values, the predicted characteristic values, or the agricultural characteristic values, and the sensed crop composition by the in-field sensor 208. For example, the prediction model generator 210 can generate a predictive crop composition model based on the vegetation index values, the historical crop composition values, the prior operational characteristic values, the soil property values, the predicted characteristic values, or the agricultural characteristic values, and the sensed crop composition values indicated by the sensor signals obtained from the in-field sensor 208.

[0118] At block 386, the predictive crop composition model, e.g., the predictive crop composition model 350, is provided to the prediction map generator 212 that generates a functional predictive composition map based on the vegetation index map 332, the historical crop composition map 333, the prior operational characteristic map 341, the soil property map 343, the predicted map 353, or the agricultural characteristic map 347, and the crop composition model 350 that maps the predicted crop composition values to different geographic locations in the field. For example, in some examples, the functional predictive crop composition map 360 predicts crop composition values. In other examples, the functional predictive crop composition map 360 predicts other items. Further, the functional predictive crop composition map 360 can be generated during the course of an agricultural harvesting operation. Thus, the functional predictive crop composition map 360 is generated as an agricultural harvester is moving through a field performing an agricultural harvesting operation.

[0119] At block 394, the prediction map generator 212 outputs the functional predictive crop composition map 360. At block 393, the prediction map generator 212 configures the functional predictive crop composition map 360 for use by the control system 214. At block 395, the prediction map generator 212 can also provide the map 360 to the control zone generator 213 for use in the generation and merging of control zones. At block 397, the prediction map generator 212 also otherwise configures the map 360. The functional predictive crop composition map 360, with or without control zones, is provided to the control system 214. At block 396, the control system 214 generates control signals based on the functional predictive crop composition map 360, with or without control zones, to control the controllable subsystems 216.

[0120] The control system 214 can generate control signals to control one or more header or other machine actuators 248, for example, to control the position or spacing of a cover plate. The control system 214 can generate control signals to control a propulsion subsystem 250. The control system 214 can generate control signals to control a steering subsystem 252. The control system 214 can generate control signals to control a residue subsystem 138. The control system 214 can generate control signals to control a machine cleaning subsystem 254. The control system 214 can generate control signals to control the threshing machine 110. The control system 214 can generate control signals to control a material handling subsystem 125. The control system 214 can generate control signals to control a crop cleaning subsystem 118. The control system 214 can generate control signals to control the communication system 206. The control system 214 can generate control signals to control the operator interface mechanism 218. The control system 214 can generate control signals to control various other controllable subsystems 256.

[0121] In an example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the header / reel controller 238 controls the header or other machine actuator 248 to control the height, tilt, or roll of the header 102. In an example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the feed rate controller 236 controls the propulsion subsystem 250 to control the travel speed of the agricultural harvester 100. In an example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the path planning controller 234 controls the steering subsystem 252 to cause the agricultural harvester 100 to steer. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the residue system controller 244 controls the residue subsystem 138. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the setting controller 232 controls the threshing machine settings of the threshing machine 110. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the setting controller 232 or another controller 246 controls the material handling subsystem 125. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the crop cleaning subsystem 118 is controlled by the setting controller 232. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the machine cleaning controller 245 controls the machine cleaning subsystem 254 on the agricultural harvester 100. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the communication system controller 229 controls the communication system 206. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the operator interface controller 231 controls the operator interface mechanism 218 on the agricultural harvester 100. In another example, the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the cover position controller 242 controls the machine / header actuator 248 to control the covers on the agricultural harvester 100. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the conveyor belt controller 240 controls the machine / header actuator 248 to control the conveyor belts on the agricultural harvester 100. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the other controller 246 controls other controllable subsystems 256 on the agricultural harvester 100.

[0122] As can be seen, the system takes characteristics such as vegetation index values, historical crop composition values, prior operational characteristic values, soil property values, predicted characteristic values, or agricultural characteristic values and maps them to a map of different locations in a field. The system also uses one or more in-field sensors that sense in-field sensor data indicative of characteristics such as crop composition values, and generates a model that models relationships between crop composition values sensed using the in-field sensors in the field and the characteristics values mapped in the map. Thus, the system uses the model and the map to generate a functional prediction map, and the generated functional prediction map can be configured for use by a control system, or presented to a local or remote operator or other user. For example, the control system can use the map to control one or more systems of a combine harvester.

[0123] Figure 6A is Figure 1 A block diagram of an example portion of an agricultural harvester 100 is shown. In particular, among other things, Figure 6A Examples of the prediction model generator 210 and the prediction map generator 212 are shown. In the illustrated example, the prior information map 258 is a historical crop composition map 333. The historical crop composition map 333 can include historical crop composition values at various locations in the field from prior operations on the field (e.g., from a previous harvesting operation on the field). Figure 6A It is also shown that the prediction model generator 210 and the prediction map generator can alternatively or additionally receive a prior information map 258, a predicted crop composition map, such as a functional predicted historical crop composition map 360. The functional predicted crop composition map 360 can be used in the model generator 210 similarly to the prior information map 258, which models relationships between information provided by the functional predicted crop composition map 360 and characteristics sensed by the in-field sensors 208, and thus the map generator 212 can use the model to generate a functional prediction map that predicts characteristics sensed by the in-field sensors 208 at different locations in the field, or indicative of sensed characteristics, based on one or more values in the functional predicted crop composition map 360 at different locations in the field and based on the prediction model. Figure 6A As shown, the prediction model generator 210 and the map generator 212 can also receive other maps 401, such as other prior information maps or other predicted maps, such as other predicted crop composition maps generated in other ways than the functional predicted crop composition map 360.

[0124] Additionally, in the example shown in Figure 6A The in-field sensors 208 can include one or more agricultural characteristic sensors 402, operator input sensors 404, and processing systems 406, in the example shown. The in-field sensors 208 can also include other sensors 408.

[0125] The agricultural property sensors 402 sense values indicative of agricultural properties. The operator input sensors 404 sense various operator inputs. The inputs can be setting inputs used to control settings on the agricultural harvester 100 or other control inputs such as steering inputs and other inputs. Thus, when the operator 260 changes a setting or provides a command input through the operator interface mechanism 218, such input is detected by the operator input sensors 404, which provide sensor signals indicative of the sensed operator input.

[0126] The processing system 406 can receive sensor signals from one or more of the agricultural property sensors 402 and the operator input sensors 404 and generate outputs indicative of sensed properties. For example, the processing system 406 can receive sensor inputs from the agricultural property sensors 402 and generate outputs indicative of agricultural properties. The processing system 406 can also receive inputs from the operator input sensors 404 and generate outputs indicative of sensed operator inputs.

[0127] The predictive model generator 210 can include a crop composition to agricultural property model generator 410 and a crop composition to command model generator 414. In other examples, the predictive model generator 210 can include more, fewer, or other model generators 415. For example, the predictive model generator 210 can include a specific agricultural property model generator or a specific operator command model generator, such as a model generator that uses a specific agricultural property (e.g., biomass or a biomass property, yield or a yield property, and various other agricultural properties) or a model generator that uses a specific operator command (e.g., a header height setting, a steering setting (e.g., a direction of travel), a speed setting (e.g., a speed of travel), a threshing cylinder setting, and other various machine settings). The predictive model generator 210 can receive the geographic location indicators 334 from the geographic location sensors 204 and generate predictive models 426 that model a relationship between information in one or more of the prior information maps 258 or the functional predictive crop composition maps 360 and one or more of the agricultural properties sensed by the sensors 402 and the operator input commands sensed by the operator input sensors 404.

[0128] The crop composition to agricultural property model generator 410 generates models that model a relationship between crop composition values (which can be on the predictive crop composition maps 360, the historical crop composition maps 333, or other maps 401) and agricultural properties sensed by the agricultural property sensors 402. The crop composition to agricultural property model generator 410 generates predictive models 426 that correspond to this relationship.

[0129] Crop composition to operator command model generator 414 generates a model that models the relationship between composition scores, which are reflected in the predicted crop composition map 360, the historical crop composition map 333 or other map 401, and the operator input commands sensed by the operator input sensor 404. Crop composition to operator command model generator 422 generates a prediction model 426 corresponding to this relationship.

[0130] Other model generators 415 may include, for example, specific agricultural characteristic model generators or characteristic operator command model generators, such as model generators using specific agricultural characteristics (e.g., biomass or biomass characteristics, yield or yield characteristics, and various other agricultural characteristics), or model generators using specific operator commands (e.g., header height settings, steering settings, speed settings, threshing drum settings, and various other machine settings).

[0131] The prediction model 426 generated by the prediction model generator 210 may include one or more prediction models generated by the crop component to agricultural characteristic model generator 410 and the crop component to operator command model generator 414, as well as other model generators that may be included in other items 415.

[0132] exist Figure 6A In one example, the prediction graph generator 212 includes a prediction agricultural characteristic graph generator 416 and a prediction operator command graph generator 422. In other examples, the prediction graph generator 212 may include additional, fewer, or other graph generators 424.

[0133] The predictive agricultural trait map generator 416 receives a prediction model 426 that models the relationship between crop component values ​​and agricultural traits sensed by agricultural trait sensors 402 (e.g., a prediction model generated by the crop component to agricultural trait model generator 410), and one or more prior information maps 258 or functional predictive crop component maps 360, or other maps 401. Based on one or more prior information maps 258 or functional predictive crop component maps 360, or one or more crop component values ​​in other maps 401 at those locations in the field, and based on the prediction model 426, the predictive agricultural trait map generator 416 generates a functional predictive agricultural trait map 427 that predicts agricultural trait values ​​(or the agricultural traits indicated by those values) at different locations in the field.

[0134] The predicted operator command map generator 422 receives a prediction model 426 (e.g., a prediction model generated by the crop constituent to command model generator 414) that models a relationship between simulated crop constituent values and operator command inputs detected by the operator input sensors 404, and one or more of the prior information maps 258 or the functional predicted crop constituent maps 360, or other maps 401. The predicted operator command map generator 422 generates a functional predicted operator command map 440 that predicts operator command inputs at different locations in the field based on the crop constituent values at the different locations in the field based on the prior information maps 258 or the functional predicted crop constituent maps 360 or other maps 401 and based on the prediction model 426.

[0135] The prediction map generator 212 outputs one or more of the functional predicted maps 427 and 440. Each of the functional predicted maps 427 and 440 can be provided to the control zone generator 213, the control system 214, or both. The control zone generator 213 produces control zones and incorporates the control zones to provide the functional predicted map 427 with control zones or the functional predicted map 440 with control zones. Any or all of the functional predicted maps 427 and 440 (with or without control zones) can be provided to the control system 214, which generates control signals based on one or all of the functional predicted maps 427 and 440 (with or without control zones) to control one or more of the controllable subsystems 216. Any or all of the maps 436 and 440 (with or without control zones) can be provided to the operator 260 or other user.

[0136] Figure 6B A block diagram is shown that illustrates examples of real-time (in-field) sensors 208. Figure 6B Some of the sensors shown or different combinations thereof can have both the sensor 402 and the processing system 406, while other sensors can be used as referenced Figure 6A and in Figure 7 The sensors 402 described, where the processing system 406 is separate. Figure 6B Some of the possible in-field sensors 208 shown in FIG. 2 are shown and described with respect to the previous figures and are similarly numbered. Figure 6BIt is shown that the field sensors 208 can include operator input sensors 980, machine sensors 982, harvested material property sensors 984, field and soil property sensors 985, environmental characteristic sensors 987, and they can include a wide variety of other sensors 226. The operator input sensors 980 can be sensors that sense operator input through the operator interface mechanisms 218. Thus, the operator input sensors 980 can sense user movement of a joystick, a lever, a steering wheel, a button, a dial, or a pedal. The operator input sensors 980 can also sense user interaction with other operator input mechanisms, such as interaction with a touch-sensitive screen, with a microphone that utilizes voice recognition, or any of a variety of other operator input mechanisms.

[0137] The machine sensors 982 can sense different characteristics of the agricultural harvester 100. For example, as discussed above, the machine sensors 982 can include the machine speed sensor 146, the separator loss sensor 148, the clean grain camera 150, the forward view image capture mechanism 151, the loss sensor 152, or the geo-location sensor 204, examples of which are described above. The machine sensors 982 can also include machine setting sensors 991 that sense machine settings. The above references to the machine sensors 982 are incorporated by reference herein. Figure 1Some examples of machine settings are described. A front end equipment (e.g., header) position sensor 993 can sense a position of the header 102, reel 164, cutter 104, or other front end equipment relative to a frame of the agricultural harvester 100 or relative to the ground. For example, the sensor 993 can sense a height of the header 102 above the ground. The machine sensors 982 can also include a front end equipment (e.g., header) orientation sensor 995. The sensor 995 can sense an orientation of the header 102 relative to the agricultural harvester 100 or relative to the ground. The machine sensors 982 can include a stability sensor 997. The stability sensor 997 senses a vibration or bounce motion (and amplitude) of the agricultural harvester 100. The machine sensors 982 can also include a residue setting sensor 999 configured to sense whether the agricultural harvester 100 is configured to chop residue, produce a windrow, or process residue in another manner. The machine sensors 982 can include a clean room fan speed sensor 951 that senses a speed of the clean fan 120. The machine sensors 982 can include a concave gap sensor 953 that senses a gap between the cylinder 112 and the concave 114 on the agricultural harvester 100. The machine sensors 982 can include a chaffer gap sensor 955 that senses a size of openings in the chaffer 122. The machine sensors 982 can include a threshing cylinder speed sensor 957 that senses a cylinder speed of the cylinder 112. The machine sensors 982 can include a cylinder force sensor 959 that senses a force used to drive the cylinder 112. The machine sensors 982 can include a screen gap sensor 961 that senses a size of openings in the screen 124. The machine sensors 982 can include a MOG moisture sensor 963 that senses a moisture level of MOG passing through the agricultural harvester 100. The machine sensors 982 can include a machine orientation sensor 965 that senses an orientation of the agricultural harvester 100. The machine sensors 982 can include a material feed rate sensor 967 that senses a feed rate of material as it travels through the feed house 106, clean grain elevator 130, or elsewhere in the agricultural harvester 100. The machine sensors 982 can include a biomass sensor 969 that senses biomass traveling through the feed house 106, the separator 116, or elsewhere in the agricultural harvester 100. The machine sensors 982 can include a fuel consumption sensor 971 that senses a rate of fuel consumption of the agricultural harvester 100 over time. The machine sensors 982 can include a power usage sensor 973 that senses power utilization in the agricultural harvester 100 (such as which subsystems are utilizing power), or a rate at which the subsystems are using power, or a power distribution between subsystems in the agricultural harvester 100. The machine sensors 982 can include a tire pressure sensor 977 that senses an inflation pressure in the tires 144 of the agricultural harvester 100. The machine sensors 982 can include a variety of other machine performance or machine characteristic sensors (such as shown in block 975).Machine performance and machine characteristic sensors 975 can sense machine performance or characteristics of the agricultural harvester 100.

[0138] The harvested material property sensors 984 can sense characteristics of the severed crop material as the crop material is being processed by the agricultural harvester 100. Crop properties can include things such as crop type, crop constituents (e.g., starch, oil, and protein), crop moisture, grain quality (such as broken grain), MOG levels, grain constituents (such as starch, oil, and protein), MOG moisture, and other crop material properties. Other sensors can sense stalk “toughness,” adhesion of corn to the ear, and other characteristics that can be beneficially used to control processing for better grain capture, reduced grain damage, reduced power consumption, reduced grain loss, and the like.

[0139] Field and soil property sensors 985 can sense characteristics of the field and soil. Field and soil properties can include soil moisture, soil compaction, presence and location of standing water, soil type, and other soil and field characteristics.

[0140] Environmental characteristic sensors 987 can sense one or more environmental characteristics. Environmental characteristics can include things such as wind direction and speed, precipitation, fog, dust levels or other obscuring matter, or other environmental characteristics.

[0141] Figure 7 A flowchart illustrating one example of the operations of the prediction model generator 210 and the prediction map generator 212 in generating one or more prediction models 426 and one or more functional prediction maps 427 and 440 is shown. At block 442, the prediction model generator 210 and the prediction map generator 212 receive a map. The map received by the prediction model generator 210 or the prediction map generator in generating one or more prediction models 426 and one or more functional prediction maps 427 and 440 can be the prior information map 258, e.g., a historical crop constituent map 333 created using data obtained during a previous operation (e.g., a previous harvesting operation) in the field. The map received by the prediction model generator 210 or the prediction map generator in generating one or more prediction models 426 and one or more functional prediction maps 427 and 440 can be the functional prediction crop constituent map 360. Other maps can also be received, as indicated by block 401, e.g., other prior information maps or other prediction maps, e.g., other predictive crop constituent maps generated in a manner different than the functional prediction crop constituent map 360.

[0142] At block 444, the prediction model generator 210 receives sensor signals containing sensor data from the field sensors 208. The field sensors can be one or more of the agricultural property sensors 402 and the operator input sensors 404. The agricultural property sensors 402 sense agricultural properties. The operator input sensors 404 sense operator input commands. The prediction model generator 210 can also receive other field sensor inputs, as shown by block 408.

[0143] At block 454, the processing system 406 processes the data contained in the one or more sensor signals or the one or more sensor signals received from the field sensors 208 to obtain processed data 409, as shown by block 410. The data contained in the one or more sensor signals can be in a raw format that is processed to receive the processed data 409. For example, a temperature sensor signal includes resistance data that can be processed into temperature data. In other examples, the processing can include digitizing, encoding, formatting, scaling, filtering, or classifying the data. The processed data 409 can be indicative of one or more of the agricultural properties or the operator input commands. The processed data 409 is provided to the prediction model generator 210. Figure 6A

[0144] Returning to Figure 7 At block 456, the prediction model generator 210 also receives the geographic location 334 or an indication of the geographic location from the geographic location sensor 204, as shown by block 408. The geographic location 334 can be related to the geographic location from which the sensed variable or variables sensed by the field sensors 208 are taken. For example, the prediction model generator 210 can obtain the geographic location 334 from the geographic location sensor 204 and determine, based on machine delays, machine speeds, and the like, the precise geographic location from which the processed data 409 is taken. Figure 6A

[0145] At block 458, the prediction model generator 210 generates one or more prediction models 426 that model a relationship between the mapped values in the received graph and the properties represented in the processed data 409. For example, in some cases, the mapped values in the received graph can be crop composition values, such as amounts of protein, starch, oil, or other compositions in the crop plant, or amounts of protein, starch, oil, or other compositions in the grain of the crop plant, and the prediction model generator 210 generates a prediction model using the mapped values in the received graph and the properties sensed by the field sensors 208 (e.g., represented in the processed data 409) or related properties (e.g., properties related to the properties sensed by the field sensors 208).

[0146] ​​The one or more predictive models 426 are provided to the predictive map generator 212. At block 466, the predictive map generator 212 generates one or more functional predictive maps. The functional predictive maps can be a functional predictive agricultural characteristic map 427 and a functional predictive operator command map 440, or any combination of these maps. The functional predictive agricultural characteristic map 427 predicts values of an agricultural characteristic (or an agricultural characteristic indicated by the values) at different locations in the field. The functional predictive operator command map 440 predicts desired or likely operator command inputs at different locations in the field. Further, one or more of the functional predictive maps 427 and 440 can be generated in the course of the agricultural operation. Thus, as the agricultural harvester 100 moves through the field performing the agricultural operation, one or more of the predictive maps 427 and 440 are generated as the agricultural operation is performed.

[0147] At block 468, the predictive map generator 212 outputs the one or more functional predictive maps 427 and 440. At block 470, the predictive map generator 212 can configure the maps for presentation to the operator 260 or another user and for possible interaction by the operator 260 or another user. At block 472, the predictive map generator 212 can configure the maps for use by the control system 214. At block 474, the predictive map generator 212 can provide the one or more predictive maps 427 and 440 to the control zone generator 213 for use in generating control zones. At block 476, the predictive map generator 212 otherwise configures the one or more predictive maps 427 and 440. In examples where the one or more functional predictive maps 427 and 440 are provided to the control zone generator 213, the one or more functional predictive maps 427 and 440 along with the control zones included therein (represented by the respective maps 265, as described above) can be presented to the operator 260 or another user, or also provided to the control system 214.

[0148] At block 478, the control system 214 then generates control signals to control the controllable subsystems based on the one or more functional predictive maps 427 and 440 (or the functional predictive maps 427 and 440 with the control zones) and from the geo-location sensor 204.

[0149] The control system 214 can generate control signals to control the cutter table or other machine actuators 248, for example to control the position or spacing of the cover plates. The control system 214 can generate control signals to control the propulsion subsystem 250. The control system 214 can generate control signals to control the steering subsystem 252. The control system 214 can generate control signals to control the residue subsystem 138. The control system 214 can generate control signals to control the machine cleaning subsystem 254. The control system 214 can generate control signals to control the threshing machine 110. The control system 214 can generate control signals to control the material handling subsystem 125. The control system 214 can generate control signals to control the crop cleaning subsystem 118. The control system 214 can generate control signals to control the communication system 206. The control system 214 can generate control signals to control the operator interface mechanism 218. The control system 214 can generate control signals to control various other controllable subsystems 256.

[0150] In examples where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the header / reel controller 238 controls the header or other machine actuator 248 to control the height, tilt, or roll of the header 102. In examples where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the feed rate controller 236 controls the propulsion subsystem 250 to control the travel speed of the agricultural harvester 100. In examples where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the path planning controller 234 controls the steering subsystem 252 to maneuver the agricultural harvester 100. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the residue system controller 244 controls the residue subsystem 138. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the setting controller 232 controls the threshing machine settings of the threshing machine 110. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the setting controller 232 or another controller 246 controls the material handling subsystem 125. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the setting controller 232 controls the crop cleaning subsystem 118. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the machine cleaning controller 245 controls the machine cleaning subsystem 254 on the agricultural harvester 100. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the communication system controller 229 controls the communication system 206. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the operator interface controller 231 controls the operator interface mechanism 218 on the agricultural harvester 100. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the cover position controller 242 controls the machine / header actuator 248 to control the covers on the agricultural harvester 100. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the belt conveyor controller 240 controls the machine / header actuator 248 to control the belt conveyor belts on the agricultural harvester 100. In another example where the control system 214 receives a functional prediction map or a functional prediction map augmented with control zones, the other controller 246 controls other controllable subsystems 256 on the agricultural harvester 100.

[0151] Figure 8A block diagram is shown that illustrates one example of a control zone generator 213. The control zone generator 213 includes a work machine actuator (WMA) selector 486, a control zone generation system 488, and a dynamic (regime) zone generation system 490. The control zone generator 213 can also include other items 492. The control zone generation system 488 includes a control zone criteria identifier component 494, a control zone boundary definition component 496, a target setting identifier component 498, and other items 520. The dynamic zone generation system 490 includes a dynamic zone criteria identification component 522, a dynamic zone boundary definition component 524, a setting 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 respective operations will first be provided.

[0152] The agricultural harvester 100 or other work machine can have multiple different types of controllable actuators that perform different functions. The controllable actuators on the agricultural harvester 100 or other work machine are collectively referred to as work machine actuators (WMAs). Each WMA can be independently controlled based on values on the functional prediction map, or the WMAs can be controlled in groups based on one or more values on the functional prediction map. Thus, the control zone generator 213 can generate control zones that correspond to each individually controllable WMA, or to groups of WMAs that are controlled in coordination with each other.

[0153] The WMA selector 486 selects a WMA or group of WMAs for which a corresponding control zone is to be generated. The control zone generation system 488 then generates a control zone for the selected WMA or group of WMAs. Different criteria can be used in identifying the control zone for each WMA or group of WMAs. For example, for one WMA, the WMA response time can be used as a criterion for defining the boundaries of the control zone. In another example, the wear characteristics (e.g., how much a particular actuator or mechanism wears out due to its movement) can be used as a criterion for identifying the boundaries of the control zone. The control zone criteria identifier component 494 identifies the particular criteria that will be used to define the control zone for the selected WMA or group of WMAs. The control zone boundary definition component 496 processes the values on the functional prediction map in the analysis to define the boundaries of the control zone on the functional prediction map in the analysis based on the values on the functional prediction map in the analysis and based on the control zone criteria for the selected WMA or group of WMAs.

[0154] The target setting identifier component 498 sets a value of a target setting that is used to control the WMA or group of WMAs in the different control zones. For example, if the selected WMA is the crop cleaning system 118, and the functional prediction map under analysis is the functional prediction crop composition map 360, then the target setting in each control zone can be a target speed setting for the cleaning fan 120 based on the crop composition values contained in the functional prediction crop composition map 360.

[0155] In some examples, where the agricultural harvester 100 is controlled based on the current or future location of the agricultural harvester 100, multiple target settings can be possible for the WMA at a given location. In such a case, the target settings can have different values and can compete with each other. Thus, the target settings need to be resolved so that only a single target setting is used to control the WMA. For example, where the WMA is an actuator in the propulsion system 250 that is controlled to control the speed of the agricultural harvester 100, there can be multiple different competing sets of criteria that are considered by the control zone generation system 488 when identifying the control zones and the target setting for the selected WMA in the control zone. For example, different target settings for controlling the speed of the cleaning fan can be based on, for example, detected or predicted crop composition values, historical crop composition values, detected or predicted agricultural property values, detected or predicted vegetation index values, predicted property values (e.g., predicted biomass, predicted yield, or predicted crop moisture), detected or predicted soil property values (e.g., amount of nitrogen in the soil), detected or predicted prior operating property values (e.g., material application property values, such as fertilizer application property values), detected or predicted feed rate values, detected or predicted fuel efficiency values, detected or predicted grain loss values, or combinations of these. It is noted that these are examples only, and the target setting for various WMAs can be based on various other values or combinations of values. However, the cleaning fan 120 cannot operate at multiple speeds at the same time. Rather, the cleaning fan 120 operates at a single speed at any given time. Thus, one of the competing target settings is selected to control the speed of the cleaning fan 120.

[0156] Accordingly, in some examples, the dynamic zone generation system 490 generates dynamic zones to address a plurality of different competing objective settings. The dynamic zone criteria identification component 522 identifies criteria for establishing a dynamic zone on the analyzed functional prediction map for a selected WMA or WMA group. Some criteria that can be used to identify or define a dynamic zone include, for example, a crop composition value, an agricultural property value, a prior operational property value, a predicted property value, a vegetation index value, a soil property value, an operator command input, a crop type or crop variety (e.g., based on a planting map or other source of crop type or crop variety), a weed type, a weed intensity, or a crop status (e.g., whether the crop is laying down, partially laying down, or standing up). These are just some examples of criteria that can be used to identify or define a dynamic zone. Just as each WMA or WMA group can have a corresponding control zone, different WMAs or WMA groups can have corresponding dynamic zones. The dynamic zone boundary definition component 524 identifies the boundaries of a dynamic zone on the analyzed functional prediction map based on the dynamic zone criteria identified by the dynamic zone criteria identification component 522.

[0157] In some examples, dynamic zones can overlap one another. For example, a crop variety dynamic zone can overlap some or all of a crop status dynamic zone. In such examples, different dynamic zones can be assigned a priority level, such that in the event of overlap between two or more dynamic zones, the dynamic zone having a higher level position or importance in the priority level is prioritized over the dynamic zone having a lower level position or importance in the priority level. The priority level of a dynamic zone can be manually set or automatically set using a rules-based system, a model-based system, or other system. As one example, in the event of overlap between a laying crop dynamic zone and a crop variety dynamic zone, the laying crop dynamic zone can be assigned a higher importance in the priority level than the crop variety dynamic zone, such that the laying crop dynamic zone is prioritized.

[0158] Further, for a given WMA or WMA group, each dynamic zone can have a unique setting resolver. The setting resolver identifier component 526 identifies a particular setting resolver for each dynamic zone identified on the analyzed functional prediction map and identifies a particular setting resolver for a selected WMA or WMA group.

[0159] Once a setting resolver for a particular dynamic zone is identified, the setting resolver can be used to resolve a competing target setting in which more than one target setting is identified based on the control zone. Different types of setting resolvers can have different forms. For example, a setting resolver identified for each dynamic zone can include a human selection resolver in which the competing target setting is presented to an operator or other user for resolution. In another example, the setting resolver can include a neural network or other artificial intelligence or machine learning system. In this case, the setting resolver can resolve the competing target setting based on a predicted quality metric or historical quality metric corresponding to each of the different target settings. As an example, an increased cleaning fan can reduce loss but increase grain quality. A reduced cleaning fan speed setting can reduce grain loss but can increase grain quality. When grain loss is selected as the quality metric, the predicted value or historical value of the selected quality metric can be used to resolve the cleaning fan speed setting given two competing vehicle speed setting values. In some cases, the setting resolver can be a set of threshold rules that can be used in place of or in addition to the dynamic zones. An example of a threshold rule can be expressed as follows:

[0160] If the predicted crop composition level value within 20 feet of the header of the agricultural harvester 100 is greater than x (where x is a selected or predetermined value), use the target setting value selected based on grain loss instead of other competing target setting values, otherwise use the target setting value based on grain quality instead of other competing target setting values.

[0161] The setting resolver can be a logical component that executes logical rules in identifying the target setting. For example, the setting resolver can resolve the target setting while attempting to minimize harvest time or minimize total harvest cost or maximize harvested grain, or based on other variables calculated as a function of the different candidate target settings. The harvest time can be minimized when the amount of harvest completed is reduced to or below a selected threshold. The total harvest cost can be minimized when the total harvest cost is reduced to or below a selected threshold. The harvested grain can be maximized when the amount of grain harvested is increased to or above a selected threshold.

[0162] Figure 9 is a flowchart showing one example of the operation of the control zone generator 213 in generating control zones and dynamic zones for a map for zone processing received by the control zone generator 213 (e.g., a map for analysis in a map).

[0163] At block 530, the control zone generator 213 receives the graph under analysis for processing. In one example, as indicated by block 532, the graph under analysis is a functional prediction graph. For example, the graph under analysis can be the functional prediction graph 360, 427, or 440. Block 534 indicates that the graph under analysis can also be other graphs.

[0164] At block 536, the WMA selector 486 selects a WMA or group of WMAs for which to generate a control zone on the graph under analysis. At block 538, the control zone criteria identification component 494 obtains the control zone definition criteria for the selected WMA or group of WMAs. Block 540 indicates such an example in which the control zone criteria is or includes the wear characteristics of the selected WMA or group of WMAs. Block 542 indicates such an example in which the control zone definition criteria is or includes the magnitude and variation of input source data, such as the magnitude and variation of values on the graph under analysis or the magnitude and variation of inputs from various field sensors 208. Block 544 indicates such an example in which the control zone definition criteria is or includes physical machine characteristics, such as the physical dimensions of the machine, the speed at which different subsystems operate, or other physical machine characteristics. Block 546 indicates such an example in which the control zone definition criteria is or includes the responsiveness of the selected WMA or group of WMAs in reaching a set value of a new command. Block 548 indicates such an example in which the control zone definition criteria is or includes machine performance indicators. Block 550 indicates such an example in which the control zone definition criteria is or includes including operator preferences. Block 552 indicates such an example in which the control zone definition criteria is also or includes other items. Block 549 indicates such an example in which the control zone definition criteria is time-based, meaning that the agricultural harvester 100 will not cross the boundary of a control zone until a selected amount of time has passed since the agricultural harvester 100 entered a particular control zone. In some cases, the selected amount of time can be a minimum amount of time. Thus, in some cases, the control zone definition criteria can prevent the agricultural harvester 100 from crossing the boundary of a control zone until at least the selected amount of time has passed. Block 551 represents such an example in which the control zone definition criteria is based on a selected size value. For example, a control zone definition criteria based on a selected size value can exclude the definition of control zones that are smaller than the selected size. In some cases, the selected size can be a minimum size.

[0165] At block 554, the dynamic zone criteria identification component 522 obtains the dynamic zone definition criteria for the selected WMA or WMA group. Block 556 represents an example in which the dynamic zone definition criteria is based on manual input from the operator 260 or another user. Block 558 illustrates an example in which the dynamic zone definition criteria is based on crop composition values, including detected, predicted, or historical crop composition values. Block 559 illustrates an example in which the dynamic zone definition criteria is based on vegetation index values. Block 560 represents an example in which the dynamic zone definition criteria is based on prior operational characteristic values. Block 561 represents an example in which the dynamic zone definition criteria is based on soil property values. Block 564 represents an example in which the dynamic zone definition criteria is or includes other criteria, such as various agricultural characteristic values, e.g., multiple predicted characteristic values, such as predicted yield values, predicted biomass values, or predicted crop moisture values.

[0166] At block 566, the control zone boundary definition component 496 generates the boundaries of the control zones on the map under analysis based on the control zone criteria. The dynamic zone boundary definition component 524 generates the boundaries of the dynamic zones on the map under analysis based on the dynamic zone criteria. Block 568 indicates an example in which zone boundaries are identified for both the control zones and the dynamic zones. Block 570 shows the target setting identifier component 498 identifying the target setting for each of the control zones. The control zones and dynamic zones can also be generated in other ways, and this is indicated by block 572.

[0167] At block 574, the setting resolver identifier component 526 identifies the setting resolver for the selected WMA in each dynamic zone defined by the dynamic zone boundary definition component 524. As discussed above, the dynamic zone resolver can be a human resolver 576, an artificial intelligence or machine learning system resolver 578, a resolver based on predicted or historical quality of each competing target setting 580, a rules-based resolver 582, a performance criteria-based resolver 584, or other resolver 586.

[0168] At block 588, the WMA selector 486 determines whether there are more WMAs or WMA groups to process. If there are additional WMAs or WMA groups to process, the process returns to block 436 in which the next WMA or WMA group for which to define control zones and dynamic zones is selected. When there are no additional WMAs or WMA groups left for which to generate control zones or dynamic zones, the process moves to block 590 in which the control zone generator 213 outputs a map for each of the WMAs or WMA groups with control zones, target settings, dynamic zones, and setting resolvers. As discussed above, the output map can be presented to the operator 260 or another user; the output map can be provided to the control system 214; or the output map can be output in other ways.

[0169] Figure 10One example of the control system 214 controlling operation of the agricultural harvester 100 based on a map output by the control zone generator 213 is shown. Thus, at block 592, the control system 214 receives a map of the work site. In some cases, the map can be a functional prediction map that can include control zones and dynamic zones (such as shown at block 594). In some cases, the received map can be a functional prediction map that excludes control zones and dynamic zones. Block 596 indicates examples in which the received map of the work site can be a priori information map with control zones and dynamic zones identified thereon. Block 598 indicates examples in which the received map can include multiple different maps or multiple different map layers. Block 610 indicates examples in which the received map can also take other forms.

[0170] At block 612, the control system 214 receives sensor signals from the geo-location sensor 204. The sensor signals from the geo-location sensor 204 can include data indicative of a geo-location 614 of the agricultural harvester 100, a speed 616 of the agricultural harvester 100, a heading 618 of the agricultural harvester 100, or other information 620. At block 622, the zone controller 247 selects a dynamic zone, and at block 624, the zone controller 247 selects a control zone on the map based on the geo-location sensor signals. At block 626, the zone controller 247 selects a WMA or group of WMAs to be controlled. At block 628, the zone controller 247 obtains one or more target settings for the selected WMA or group of WMAs. The target settings obtained for the selected WMA or group of WMAs can come from a variety of different sources. For example, block 630 shows examples in which one or more of the target settings for the selected WMA or group of WMAs are based on input from the control zone on the map of the work site. Block 632 shows examples in which one or more of the target settings are obtained from manual input by the operator 260 or another user. Block 634 shows examples in which the target settings are obtained from the field sensors 208. Block 636 shows examples in which one or more of the target settings are obtained from one or more sensors on other machines simultaneously working in the same field as the agricultural harvester 100 or from one or more sensors on machines that have worked in the same field in the past. Block 638 shows examples in which the target settings are also obtained from other sources.

[0171] At block 640, the zone controller 247 accesses the selected dynamic zone’s setting resolver and controls the setting resolver to resolve the competition target setting into a resolved target setting. As discussed above, in some cases, the setting resolver can be a human resolver, in which case the zone controller 247 controls the operator interface mechanism 218 to present the competition target setting to the operator 260 or another user for resolution. In some cases, the setting resolver can be a neural network or other artificial intelligence or machine learning system, and the zone controller 247 submits the competition target setting to the neural network, artificial intelligence, or machine learning system for selection. In certain cases, the setting resolver can be based on a predicted quality indicator or historical quality indicators, based on threshold rules, or based on a logic component. In any of these latter examples, the zone controller 247 executes the setting resolver to obtain the resolved target setting based on the predicted quality indicator or historical quality indicators, based on the threshold rules, or in the case of using a logic component.

[0172] At block 642, in the case that the zone controller 247 has identified a resolved target setting, the zone controller 247 provides the resolved target setting to other controllers in the control system 214 that generate control signals based on the resolved target setting and apply the control signals to the selected WMA or WMA group. For example, in the case that 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 additional WMAs or additional WMA groups are to be controlled at the current geographic location of the agricultural harvester 100, such as detected at block 612, the process returns to block 626 at which 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 agricultural harvester 100 have been addressed. If no additional WMAs or WMA groups remain to be controlled at the current geographic location of the agricultural harvester 100, the process proceeds to block 646 at which the zone controller 247 determines whether additional control zones to be considered exist in the selected dynamic zone. If additional control zones to be considered exist, the process returns to block 624 at which the next control zone is selected. If no additional control zones need to be considered, the process proceeds to block 648 at which a determination is made as to whether additional dynamic zones still need to be considered. The zone controller 247 determines whether additional dynamic zones still need to be considered. If additional dynamic zones still need to be considered, the process returns to block 622 at which the next dynamic zone is selected.

[0173] At block 650, the zone controller 247 determines whether the operation being performed by the agricultural harvester 100 is complete. If no, the zone controller 247 determines whether the control zone criteria have been met to continue processing, as shown at block 652. For example, as mentioned above, the control zone definition criteria can include criteria defining when the agricultural harvester 100 can cross the control zone boundary. For example, whether the agricultural harvester 100 can cross the control zone boundary can be defined by a selected time period, meaning that the agricultural harvester 100 is prevented from crossing the zone boundary until the selected amount of time has elapsed. In this case, at block 652, the zone controller 247 determines whether the selected time period has elapsed. Additionally, the zone controller 247 can continuously perform the processing. Thus, the zone controller 247 does not wait for any particular time period before continuing to determine whether the operation of the agricultural harvester 100 is complete. At block 652, the zone controller 247 determines that it is time to continue processing, and then processing continues at block 612, where the zone controller 247 again receives input from the geo-location sensor 204. It should also be understood that the zone controller 247 can use a multiple-input, multiple-output controller to simultaneously control the WMAs and WMA groups, rather than sequentially controlling the WMAs and WMA groups.

[0174] Figure 11 is a block diagram illustrating one example of an operator interface controller 231. In the illustrated example, the operator interface controller 231 includes an operator input command processing system 654, other controller interaction systems 656, a speech processing system 658, and an action signal generator 660. The operator input command processing system 654 includes a speech handling system 662, a touch gesture processing system 664, and other items 666. The other controller interaction systems 656 include a controller input processing system 668 and a controller output generator 670. The speech processing system 658 includes a trigger detector 672, a recognition component 674, a synthesis component 676, a natural language understanding system 678, a dialog management system 680, and other items 682. The action 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. A brief description of some of the items in the operator interface controller 231 and their associated operations is provided before describing the operation of the example operator interface controller 231 in handling various operator interface actions. Figure 11 A brief description of some of the items in the operator interface controller 231 and their associated operations is provided before describing the operation of the example operator interface controller 231 in handling various operator interface actions.

[0175] The operator input command processing system 654 detects operator inputs on the operator interface mechanisms 218 and processes these command inputs. The speech handling system 662 detects speech inputs and processes interactions with the speech processing system 658 to process speech command inputs. The touch gesture processing system 664 detects touch gestures on touch sensitive elements in the operator interface mechanisms 218 and processes these command inputs.

[0176] Other controller interaction system 656 processes interactions with other controllers in the control system 214. The controller input processing system 668 detects and processes inputs from other controllers in the control system 214 and the controller output generator 670 generates outputs and provides these outputs to other controllers in the control system 214. The speech processing system 658 recognizes speech inputs, determines the meaning of these inputs, and provides outputs indicating the meaning of the speech inputs. For example, the speech processing system 658 can recognize a speech input from the operator 260 as a set change command where the operator 260 is commanding the control system 214 to change a setting of a controllable subsystem 216. In such an example, the speech processing system 658 recognizes the content of the speech command, identifies the meaning of the command as a set change command, and provides the meaning of the input back to the speech handling system 662. The speech handling system 662 in turn interacts with the controller output generator 670 to provide command outputs to the appropriate controller in the control system 214 to complete the speech set change command.

[0177] The speech processing system 658 can be invoked in a variety of different ways. For example, in one example, the speech handling system 662 continuously provides input from a microphone (as one of the operator interface mechanisms 218) to the speech processing system 658. The microphone detects speech from the operator 260, and the speech handling system 662 provides the detected speech to the speech processing system 658. The trigger detector 672 detects a trigger that indicates that the speech processing system 658 is invoked. In some cases, when the speech processing system 658 receives continuous speech input from the speech handling system 662, the speech recognition component 674 performs continuous speech recognition on all speech spoken by the operator 260. In some cases, the speech processing system 658 is configured to be invoked using a wake word. That is, in some cases, the operation of the speech processing system 658 can be initiated based on recognition of a selected voice word, referred to as a wake word. In such examples, where the recognition component 674 recognizes the wake word, the recognition component 674 provides an indication to the trigger detector 672 that the wake word has been recognized. The trigger detector 672 detects that the speech processing system 658 has been invoked or triggered by the wake word. In another example, the speech processing system 658 can be invoked by the operator 260 actuating an actuator on the user interface mechanism, such as by touching an actuator on a touch-sensitive display screen, by pressing a button, or by providing another trigger input. In such examples, when the trigger input via the user interface mechanism is detected, the trigger detector 672 can detect that the speech processing system 658 has been invoked. The trigger detector 672 can also detect that the speech processing system 658 has been invoked in other ways.

[0178] Once the speech processing system 658 is invoked, speech input from the operator 260 is provided to the speech recognition component 674. The speech recognition component 674 recognizes linguistic elements in the speech input, such as words, phrases, or other linguistic units. The natural language understanding system 678 identifies a meaning of the recognized speech. The meaning can be any of a natural language output, a command output that identifies a command reflected in the recognized speech, a value output that identifies a value in the recognized speech, or a variety of other outputs that reflect an understanding of the recognized speech. For example, more generally, the natural language understanding system 678 and the speech processing system 568 can understand a meaning of speech recognized in the context of the agricultural harvester 100.

[0179] In some examples, the speech processing system 658 can also generate output that is presented to the user through the user experience navigation operator 260 based on the speech input. For example, the dialog management system 680 can generate and manage a dialog with the user in order to identify what the user wants to do. The dialog box can disambiguate user commands, identify one or more particular values needed to perform the user command; or obtain other information from the user or provide other information to 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. Thus, the dialog managed by the dialog management system 680 can be exclusively a speech dialog or a combination of a visual dialog and a speech dialog.

[0180] The action signal generator 660 generates action signals to control the operator interface mechanisms 218 based on output from one or more of the operator input command processing system 654, the other controller interaction system 656, and the speech processing system 658. The visual control signal generator 684 generates control signals to control visual items in the operator interface mechanisms 218. The visual items can be lights, display screens, warning indicators, or other visual items. The audio control signal generator 686 generates output that controls audio elements of the operator interface mechanisms 218. The audio elements include speakers, audible alert mechanisms, horns, or other audible elements. The haptic control signal generator 688 generates control signals that are output to control haptic elements of the operator interface mechanisms 218. The haptic elements include vibratory elements that can be used to vibrate, for example, the operator's seat, steering wheel, pedals, or joystick used by the operator. The haptic elements can include tactile feedback or force feedback elements that provide tactile or force feedback to the operator through the operator interface mechanisms. The haptic elements can also include a wide variety of other haptic elements.

[0181] Figure 12 is a flowchart showing one example of the operation of the operator interface controller 231 in generating an operator interface display on the operator interface mechanisms 218, which can include a touch-sensitive display screen. Figure 12 One example of how the operator interface controller 231 can detect and process operator interaction with a touch-sensitive display screen is also shown.

[0182] At block 692, the operator interface controller 231 receives a map. Block 694 indicates an example in which the map is a functional prediction map, while block 696 indicates an example in which the map is another type of map. At block 698, the operator interface controller 231 receives input from the geo-location sensor 204 that identifies a geo-location of the agricultural harvester 100. As shown in block 700, the input from the geo-location sensor 204 can include a heading and a location of the agricultural harvester 100. Block 702 indicates an example in which the input from the geo-location sensor 204 includes a speed of the agricultural harvester 100, while block 704 indicates an example in which the input from the geo-location sensor 204 includes other items.

[0183] At block 706, the visual control signal generator 684 in the operator interface controller 231 controls the touch-sensitive display screen in the operator interface mechanism 218 to generate a display showing all or a portion of the field represented by the received map. Block 708 indicates that the displayed field can include a current position marker that shows a current position of the agricultural harvester 100 relative to the field. Block 710 indicates an example in which the displayed field includes a next work unit marker that identifies a next work unit (or area on the field) in which the agricultural harvester 100 will operate. Block 712 indicates an example in which the displayed field includes a coming area display portion that displays areas that have not yet been processed by the agricultural harvester 100, while block 714 indicates an example in which the displayed field includes a previously visited display portion that represents areas of the field that have already been processed by the agricultural harvester 100. Block 716 indicates an example in which the displayed field displays various characteristics of the field with geo-registered locations on the map. For example, if the received map is a crop composition map, such as the functional prediction crop composition map 360, the displayed field can show different crop composition values that exist in the field that are geo-registered within the displayed field. The mapped characteristics can be shown in previously visited areas (such as shown in block 714), coming areas (such as shown in block 712), and next work units (such as shown in block 710). Block 718 indicates an example in which the displayed field includes other items.

[0184] Figure 13 is a diagram showing one example of a user interface display 720 that can be generated on a touch-sensitive display screen. In other implementations, the user interface display 720 can be generated on other types of displays. The touch-sensitive display screen can be mounted in an operator's compartment of the agricultural harvester 100 or on a mobile device or elsewhere. Before continuing with the description of the flowchart shown in Figure 12 , the user interface display 720 will be described.

[0185] In Figure 13In the example shown in FIG. 7, the user interface display 720 shows that the touch- sensitive display includes display features for operating the microphone 722 and the speaker 724. Thus, the touch-sensitive display can be communicably coupled to the microphone 722 and the speaker 724. The box 726 indicates that the touch-sensitive display can include a variety of user interface control actuators, such as buttons, keypads, soft keypads, links, icons, switches, etc. The operator 260 can actuate the user interface control actuators to perform various functions.

[0186] In Figure 13 In the example shown in FIG. 7, the user interface display 720 includes a field display portion 728 that displays at least a portion of a field in which the agricultural harvester 100 is operating. The field display portion 728 is shown with a current position marker 708 that corresponds to a current position of the agricultural harvester 100 in the portion of the field shown in the field display portion 728. In one example, the operator can control the touch-sensitive display to zoom in on portions of the field display portion 728 or pan or scroll the field display portion 728 to display different portions of the field. A next work unit 730 is shown as the area of the field directly in front of the current position marker 708 of the agricultural harvester 100. The current position marker 708 can also be configured to identify a direction of travel of the agricultural harvester 100, a speed of travel of the agricultural harvester 100, or both. In Figure 13 In this example, the shape of the current position marker 708 provides an indication of the orientation of the agricultural harvester 100 within the field, which can be used as an indication of the direction of travel of the agricultural harvester 100.

[0187] The size of the next work unit 730 shown on the field display portion 728 can vary based on a variety of different criteria. For example, the size of the next work unit 730 can vary according to the speed of travel of the agricultural harvester 100. Thus, when the agricultural harvester 100 is traveling faster, then the area of the next work unit 730 can be larger, if the agricultural harvester 100 is traveling slower, then the area of the next work unit 730 is larger. In another example, the size of the next work unit 730 can vary according to the size of the agricultural harvester 100, including equipment on the agricultural harvester 100 (e.g., 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 portion 728 is also shown displaying a previously visited area 714 and an upcoming area 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 portion 728 is also shown displaying different characteristics of the field. In Figure 13In the example shown, the plot displayed is a predicted crop composition plot, such as a functional predicted crop composition plot 360. Therefore, multiple crop composition markers are shown on the field display section 728. A set of already visited areas 714 shows crop composition display markers 732. The next work unit 712 shows a set of crop composition display markers 732, and the next work unit 730 shows a set of crop composition display markers 732. Figure 13 The crop component display marker 732 is composed of different symbols that represent regions with similar crop component values. Figure 14 In the example shown, the ! symbol represents a region with high crop protein; the * symbol represents a region with medium crop protein; and the # symbol represents a region with low crop protein. Therefore, the field display section 728 shows different measured or predicted values ​​(or characteristics indicated by values) located in different areas of the field, and uses various display markers 732. As shown, the field display section 728 includes display markers, specifically... Figure 13 The crop component display mark 732 in the example shown is located at a specific position associated with a specific location on the field being displayed. In some cases, each location of the field may have a display mark associated with it. Therefore, in some cases, display marks may be provided at each location of the field display section 728 to identify the nature of the characteristics mapped for each specific location of the field. Thus, this disclosure includes providing display marks, such as crop component display mark 732 (e.g., at one or more locations on the field display section 728), at locations such as the field display section 728. Figure 13 In the context of this example, display markers 732 can be used to identify properties, degrees, etc., thereby identifying characteristics of corresponding locations in the displayed field. As previously mentioned, display markers 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 of the field may have a display marker associated with it. Therefore, in some cases, display markers may be provided at each location of the field display section 728 to identify the properties of the characteristics mapped to each particular location of the field. Thus, this disclosure includes providing display markers at one or more locations on the field display section 728, such as loss level display markers 732 (e.g., on...). Figure 11 (In the context of this example) to identify properties, degrees, etc., thereby identifying the characteristics at the corresponding locations in the displayed field.

[0188] In other examples, the diagrams shown may be one or more of the diagrams described herein, including infographics, prior infographics, functional prediction diagrams such as prediction diagrams or prediction control area diagrams, or other prediction diagrams, or combinations thereof. Therefore, the symbols and characteristics shown will be related to the information, data, characteristics, and values ​​provided by the one or more diagrams shown.

[0189] In Figure 13 the example shown, user interface display 720 also has a control display portion 738. Control display portion 738 allows the operator to view information and interact with user interface display 720 in various ways.

[0190] The actuators and display indicia in portion 738 can be displayed as, for example, separate items, a fixed list, a scrollable list, a drop-down menu, or a drop-down list. In Figure 13 the example shown, display portion 738 shows three different stem diameter categories corresponding to the three symbols mentioned above. Display portion 738 also includes a set of touch-sensitive actuators with which the operator 260 can interact by touching. For example, the operator 260 can touch a touch-sensitive actuator with a finger to activate the corresponding touch-sensitive actuator. As shown, display portion 738 also includes a plurality of interactive tabs of a protein tab 762, a soil tab 764, a starch tab 766, a plurality of tabs 768, and other tabs 770. Activating one of the tabs can modify the values displayed in portions 728 and 738. For example, as shown, protein tab 762 is activated, and thus the values mapped on portion 728 and displayed in portion 738 correspond to protein content values for the crop or crop component (e.g., grain). When the operator 260 touches tab 762, touch gesture processing system 664 updates portions 728 and 738 to display oil content values for the crop or crop component (e.g., grain). When the operator 260 touches tab 766, touch gesture processing system 664 updates portions 728 and 738 to display starch content values for the crop or crop component (e.g., grain). When the operator 260 touches tab 768, touch gesture processing system 664 updates portions 728 and 738 to display a combination of component content values (e.g., protein content values, oil content values, starch content values) and combinations of other component content values. When the operator 260 touches tab 770, touch gesture processing system 664 updates portions 728 and 738 to display one or more of various other component content values.

[0191] As Figure 14As shown, the display portion 738 includes an interactive flag display portion, represented generally at 741. The interactive flag display portion 741 includes a flag bar 739 that displays flags that have been automatically or manually set. A flag actuator 740 allows the operator 260 to mark a location, such as the current location of the agricultural harvester, or another location on the field designated by the operator, and add information that indicates a characteristic of it, such as the moisture of the crop found at the current location. For example, when the operator 260 actuates the flag actuator 740 by touching the flag actuator 740, the touch gesture processing system 664 in the operator interface controller 231 identifies the current location as a location where the agricultural harvester 100 encountered a high protein content. When the operator 260 touches the button 742, the touch gesture processing system 664 identifies the current location as a location where the agricultural harvester 100 encountered a medium protein content. When the operator 260 touches the button 744, the touch gesture processing system 664 identifies the current location as a location where the agricultural harvester 100 encountered a low protein content. Upon actuation of one of the flag actuators 740, 742, or 744, the touch gesture processing 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 location identified by the operator. In this way, areas of the field where the predicted values do not accurately represent the actual values can be marked for later analysis, and can also be used for machine learning. In other examples, the operator can design an area before or around the agricultural harvester 100 by actuating one of the flag actuators 740, 742, or 744, such that control of the agricultural harvester 100 can be based on values specified by the operator 260.

[0192] The display portion 738 also includes an interactive marker display portion, represented generally at 743. The interactive marker display portion 743 includes a symbol bar 746 that displays symbols (in the case of crop composition, the symbols correspond to each category of value or characteristic being tracked on the field display portion 728). The display portion 738 also includes an interactive indicator display portion, represented generally at 745. The interactive indicator display portion 745 includes an indicator bar 748 that displays indicators (which can be textual indicators or other indicators) that identify the categories of values or characteristics (in the case of crop composition, the indicators correspond to each category of value or characteristic being tracked on the field display portion 728). Figure 13 Figure 13 Without limitation, the symbols in the symbol bar 746 and the indicators in the indicator bar 748 can include any display feature, such as different colors, shapes, patterns, intensities, text, icons, or other display features, and can be customized for the agricultural harvester 100 through operator interaction.

[0193] ​The display portion 738 also includes an interactive value display portion, generally represented at 747. The interactive value display portion 747 includes a value display field 750 that displays a selected value. The selected value corresponds to the characteristic or value being tracked, or displayed, or both, on the field display portion 728. The selected value can be selected by the operator of the agricultural harvester 100. The selected value in the value display field 750 defines a range of values, or a value, by which other values, such as predicted values, are classified. Thus, in the example of Figure 13 a predicted or measured crop (or crop component, such as grain) protein content meeting or being greater than 12% is classified as "high crop protein," and a predicted or measured crop moisture meeting or being less than 8% is classified as "low crop protein." In some examples, the selected value can include a range, such that predicted or measured values within the range of the selected value will be classified under the corresponding indicator. As shown, Figure 13 "moderate crop protein" includes a range of 9-11% such that predicted or measured crop (or crop component, such as grain) protein content values falling within the range of 9-11% are classified as "moderate crop protein." The selected value in the value display field 750 can be adjusted by the operator of the agricultural harvester 100. In one example, the operator 260 can select a particular portion of the field display portion 728 for which the value displayed in the field 750 will be displayed. Thus, the value in the field 750 can correspond to the value in the display portion 712, 714, or 730.

[0194] The display portion 738 also includes an interactive threshold display portion indicated generally at 749. The interactive threshold display portion 749 includes a threshold display bar 752 that displays an action threshold. The action threshold in the bar 752 can be a threshold that corresponds to the selected value in the value display bar 750. If the predicted or measured value or both of the characteristic being tracked or displayed meets the corresponding action threshold in the threshold display bar 752, the control system 214 takes one or more actions identified in bar 754. In some cases, the measured or predicted value can meet the corresponding action threshold by reaching or exceeding the corresponding action threshold. In one example, the operator 260 can select a threshold, for example, to change the threshold by touching the threshold in the threshold display bar 752. Once selected, the operator 260 can change the threshold. The threshold in the bar 752 can be configured such that the specified action is performed when the measured or predicted value of the characteristic exceeds the threshold, equals the threshold, or is less than the threshold. In some cases, the threshold can represent a range of values, or a range of deviation from the selected value in the value display bar 750, such that a predicted or measured characteristic value that reaches or falls within the range meets the threshold. For example, in the example of crop composition, a predicted crop (or crop component, such as grain) protein content that falls within 0.5% of a 12% protein content would meet a corresponding action threshold (0.5% of 12% protein content) and an action (such as adjusting the cleaning fan speed of an agricultural harvester or adjusting a grain shaker) would be taken by the control system 214. In other examples, the threshold in the bar threshold display bar 752 is separate from the selected value in the value display bar 750, such that the value in the value display bar 750 defines a classification and display of the predicted or measured value, while the action threshold defines when an action is taken based on the measured or predicted value. For example, while a 12% predicted or measured crop (or crop component, such as grain) protein content is designated as a "high crop protein" for classification and display purposes, the action threshold can be 13%, such that an action will not be taken until the crop (or crop component, such as grain) protein content meets the threshold. In other examples, the threshold in the threshold display bar 752 can include a distance or a time. For example, in the example of distance, the threshold can be a threshold distance from a region of the field in which the measured or predicted value is georeferenced, such that the agricultural harvester 100 must be in the region before an action is taken. For example, a threshold distance value of 5 feet means that an action will be taken when the agricultural harvester is located at a position that is 5 feet or less from a region of the field in which the measured or predicted value is georeferenced. In examples where the threshold is a time, the threshold can be a threshold time for the agricultural harvester 100 to reach a region of the field in which the measured or predicted value is georeferenced.For example, a threshold of 5 seconds means that the agricultural harvester 100 will take action when it is 5 seconds away from the area of the field for which the measured or predicted value is georeferenced. In such an example, the current position and travel speed of the agricultural harvester can be considered.

[0195] The display portion 738 also includes an interaction action display portion, generally indicated at 751. The interaction action display portion 751 includes action display columns 754 that display action identifiers that indicate actions to be taken when the predicted or measured value satisfies the action threshold in the threshold display column 752. The operator 260 can touch the action identifiers in the columns 754 to change the action to be taken. The action can be taken when the threshold is satisfied. For example, at the bottom of the columns 754, a chaffer action, an adjust clean fan speed action, and a hold action are identified as actions to be taken if the measured or predicted value satisfies the threshold in the column 752. In some examples, multiple actions can be taken when the threshold is satisfied. For example, the speed of the clean fan can be adjusted (e.g., increased or decreased), the size of the openings in the chaffer can be adjusted (e.g., opened or closed). These are just some examples.

[0196] The actions that can be set in the columns 754 can be any of a variety of different types of actions. For example, the actions can include a disable action that, when executed, prevents the agricultural harvester 100 from further harvesting in an area. The actions can include a speed change action that, when executed, changes the travel speed of the agricultural harvester 100 through the field. The actions can include a setting change action to change a setting of an internal actuator or another WMA or group of WMAs, or to implement a change to a setting such as a header position setting, such as a clean fan speed setting or a chaffer gap setting, among various other settings. These are just examples, and a variety of other actions are contemplated herein.

[0197] The items shown on the user interface display 720 can be visually controlled. The visual control of the interface display 720 can be performed to capture the attention of the operator 260. For example, the items can be controlled to modify the intensity, color, or pattern of the displayed items. Additionally, the items can be controlled to flash. As an example, the described changes to the visual appearance of the items are provided. Thus, other aspects of the visual appearance of the items can be changed. Thus, the items can be modified in a desired manner in various situations in order to, for example, capture the attention of the operator 260. Furthermore, while a particular number of items are displayed on the user interface display 720, this need not be the case. In other examples, more or fewer items, including more or fewer particular items, can be included on the user interface display 720.

[0198] Now returning to Figure 12The flowchart of Figure 7 continues the description of the operation of the operator interface controller 231. At block 760, the operator interface controller 231 detects an input setting a flag and controls the touch-sensitive user interface display 720 to display the flag on the field display portion 728. The detected input can be an operator input (as shown at 762) or an input from another controller (as shown at 764). At block 766, the operator interface controller 231 detects a field sensor input from one of the field sensors 208 indicative of a measured property of the field. At block 768, the visual 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 of the actuators for setting or modifying the values in the fields 739, 746, and 748 can be displayed. Thus, the user can set the flags and modify the properties of the flags. Block 772 indicates that the action threshold in field 752 is displayed. Block 776 indicates that the action in field 754 is displayed and block 778 indicates that the selected value in field 750 is displayed. Block 780 indicates that a variety of other information and actuators can also be displayed on the user interface display 720.

[0199] At block 782, the operator input command processing system 654 detects and processes operator input corresponding to interaction with the user interface display 720 performed by the operator 260. In the case where the user interface mechanism on which the user interface display 720 is displayed is a touch-sensitive display screen, the operator interaction input by the operator 260 can be a touch gesture 784. In some cases, the operator interaction input can be input using a click device 786 or other operator interaction input device 788.

[0200] At block 790, the operator interface controller 231 receives a signal indicating an alarm condition. For example, block 792 indicates that a signal can be received by the controller input processing system 668 indicating that a measured or predicted value satisfies a threshold condition present in column 752. As explained previously, the threshold condition can include the value being below the threshold, the value being at the threshold, or the value being above the threshold. Block 794 shows that the action signal generator 660 can respond to receiving the alarm condition by generating a visual alarm using the visual control signal generator 684, generating an audio alarm using the audio control signal generator 686, generating a haptic alarm using the haptic control signal generator 688, or by using any combination of these, to alert the operator 260. Similarly, as shown by block 796, the controller output generator 670 can generate outputs to other controllers in the control system 214 so that these controllers perform corresponding actions identified in column 754. Block 798 shows that the operator interface controller 231 can also detect and handle alarm conditions in other ways.

[0201] Block 900 shows that the speech management system 662 can detect and handle inputs that invoke the speech processing system 658. Block 902 shows that performing speech processing can include using the dialog management system 680 to have a conversation with the operator 260. Block 904 shows that the speech processing can include providing signals to the controller output generator 670 to automatically perform control operations based on the speech input.

[0202] Table 1 below shows an example of a conversation 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 that is detected by the trigger detector 672. In the example shown in Table 1, the wake-up word is "Johnny."

[0203] Table 1

[0204] Operator: "Johnny, tell me the current crop composition values."

[0205] Operator console controller: "Protein content is currently high."

[0206] Operator: "Johnny, what should I do about the protein content?"

[0207] Operator interface controller: "Reduce travel speed by 1 mph to reduce grain loss."

[0208] Table 2 shows an example in which the speech synthesis component 676 provides output to the audio control signal generator 686 to provide auditory updates intermittently or periodically. The interval between updates can be time-based (such as every five minutes), or coverage or distance-based (such as every five acres), or anomaly-based (such as when a measured value is greater than a threshold).

[0209] Table 2

[0210] Operator console control: "Starch content is high over the past 10 minutes."

[0211] Operator interface console: "Starch content is predicted to be moderate over the next 1 acre."

[0212] Operator interface console: "Attention: Starch content is about to change, open the huller to capture more grain."

[0213] The example shown in Table 3 shows that some of the actuators or user input mechanisms on the touch-sensitive display 720 can be supplemented with voice dialog. The example in Table 3 shows that the action signal generator 660 can generate action signals to automatically flag crop constituent regions in a field that is being harvested.

[0214] Table 3

[0215] Person: "Johnny, flag the high oil content region."

[0216] Operator interface console: "High oil content region flagged."

[0217] The example shown in Table 4 illustrates that the action signal generator 660 can have a dialog with the operator 260 to start and stop flagging of crop constituent regions.

[0218] Table 4

[0219] Person: "Johnny, start flagging the high oil content region."

[0220] Operator interface controller: "Flagging high oil content region."

[0221] Person: "Johnny, stop flagging the high oil content region."

[0222] Operator interface console: "High oil content region flagging has stopped."

[0223] The example shown in Table 5 shows that the action signal generator 160 can generate signals to flag crop constituent regions in a different manner than shown in Tables 3 and 4.

[0224] Table 5

[0225] Human: "Johnny, mark the next 100 feet as a low protein content area."

[0226] Operator console controller: "Mark the next 100 feet as a low protein content area."

[0227] Returning again Figure 12 to FIG. 9, block 906 shows that the operator interface controller 231 can also detect and handle 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 that an alert or output message should be presented to the operator 260. Block 908 shows that the output can be an audio message. Block 910 shows that the output can be a visual message, and block 912 shows that the output can be a haptic message. Until the operator interface controller 231 determines that the current harvesting operation is complete, such as shown in block 914, the process returns to block 698, where the geographic position of the harvester 100 is updated, and the process continues as described above to update the user interface display 720.

[0228] Once the operation is complete, any desired values that were displayed or have been displayed on the user interface display 720 can be saved. These values can also be used in machine learning to improve different parts of the prediction model generator 210, the prediction map generator 212, the control zone generator 213, the control algorithm, or other items. The saved desired values are indicated by block 916. These values can be saved locally on the agricultural harvester 100, or the values can be saved at a remote server location or sent to another remote system.

[0229] As can be seen from this, at different geographic locations of a field being harvested, one or more maps can be obtained by an agricultural harvester that display agricultural property values, including predicted or historical crop composition values, a priori operational property values, vegetation index values, or soil composition values. As the agricultural harvester moves through the field, on-board sensors sense a property having a value indicative of the agricultural property. The prediction map generator generates a prediction map from the values of the agricultural property in the map and the agricultural property sensed by the on-board sensors that predicts control values for different locations in the field. The control system controls the controllable subsystems in accordance with the control values in the prediction map.

[0230] 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 indicative of an agricultural characteristic. A control value can be a predicted value, a measured value, or a detected value. A control value can include any of the values provided by a graph, such as any one of the graphs described herein, for example, a control value can be a value provided by an information graph, a value provided by a prior information graph, or a value provided by a prediction graph, such as a functional prediction graph. A control value can also include any of the characteristics indicated by or derived from a value detected by any one of the sensors described herein. In other examples, a control value can be provided by an operator of the agricultural machine, such as a command input by an operator of the agricultural machine.

[0231] The current discussion has mentioned processors and servers. In some examples, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. The processors and servers are a functional part of the systems or devices of which they are a part, and are activated by, and facilitate the functionality of, those systems.

[0232] Also, a number of user interface displays have been discussed. The displays can take a variety of different forms, and can have a variety of different user-actuatable operator interface mechanisms provided thereon. For example, the user-actuatable operator interface mechanisms can be text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. The user-actuatable operator interface mechanisms can also be actuated in a variety of different ways. For example, the user-actuatable operator interface mechanisms can be actuated using an operator interface mechanism such as a pointing device, (such as a trackball or mouse, a hardware button, a switch, a joystick or keyboard, a thumb switch or thumb pad, etc.), a virtual keyboard or other virtual actuator. Further, where the screen on which the user-actuatable operator interface mechanisms are displayed is a touch-sensitive screen, the user-actuatable operator interface mechanisms can be actuated using touch gestures. Also, the user-actuatable operator interface mechanisms can be actuated using voice commands using voice recognition functionality. Voice recognition can be implemented using a voice detection device such as a microphone and software for recognizing detected voice and executing commands based on received voice.

[0233] A number of data stores have also been discussed. It should be noted that the data stores can each be divided into a plurality of data stores. In some examples, one or more of the data stores can be local to the system accessing the data store, all of the data stores can be located remotely from the system utilizing the data stores, or one or more data stores can be local while others are remote. All of these configurations are contemplated by the present disclosure.

[0234] Furthermore, the attached figures illustrate a number of blocks that include functionality that is attributed to each block. It should be noted that the functionality attributed to multiple different blocks can be performed by fewer blocks, and that more functionality can be performed by more blocks. In different examples, some functionality can be added, and some functionality can be removed.

[0235] It should be noted that the above discussion has described a variety of 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 (including but not limited to artificial intelligence components, e.g., neural networks) that perform the functions associated with those systems, components, logic, or interactions, some of which are described below. Furthermore, any or all of the systems, components, logic, and interactions can be implemented by software that is loaded into memory and subsequently executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic, and interactions can also be implemented by different combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that can be used to implement any or all of the systems, components, logic, and interactions described above. Other structures can also be used.

[0236] Figure 14 is a block diagram of an agricultural harvester 600, which can be similar to the agricultural harvester 100 shown in Figure 2 The agricultural harvester 600 communicates with elements in the 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 end users to be aware of the physical location or configuration of the system that delivers the services. In various examples, the remote server can deliver services over a wide area network, such as the Internet, using appropriate protocols. For example, the remote server can deliver an application over a wide area network, and the application can be accessed through a web browser or any other computing component. Figure 2 The software or components shown in and the data associated therewith can be stored on servers at remote locations. Computing resources in the remote server environment can be consolidated at a remote data center location, or the computing resources can be dispersed to multiple remote data centers. The remote server infrastructure can deliver services through a shared data center, even though it appears as a single access point to the user. Thus, the components and functionality described herein can be provided from a remote server at a remote location using a remote server architecture. Alternatively, the components and functionality can be provided from a server, or the components and functionality can be installed directly or otherwise on a client device.

[0237] Figure 14In the example shown in FIG. 1, some of the items are similar to Figure 2 The items shown in FIG. 1 are similarly numbered. Figure 14 It is specifically shown that the prediction model generator 210 or the prediction map generator 212 or both can be located at a server location 502 remote from the agricultural harvester 600. Thus, in Figure 14 In the example shown in FIG. 1, the agricultural harvester 600 accesses the system through the remote server location 502.

[0238] Figure 14 Another example of a remote server architecture is also depicted. Figure 14 It is shown that Figure 2 Some of the elements of FIG. 1 can be arranged at the remote server location 502, while other elements can be located elsewhere. As an example, the data store 202 can be placed at a location separate from the location 502 and accessed via a remote server at the location 502. Regardless of where these elements are located, these elements can be accessed by the agricultural harvester 600 directly over a network such as a wide area network or a local area network, these elements can be hosted by a service at a remote site, or these elements can be provided as a service or accessed by a connectivity service that resides at a remote location. Further, data can be stored at any location and the stored data can be accessed or forwarded to an operator, user, or system by the operator, user, or system. For example, a physical carrier wave can be used instead of or in addition to an electromagnetic wave carrier. In some examples, another machine such as a fuel truck or other mobile machine or vehicle can have an automatic, semi-automatic, or manual information collection system in the event of poor or non-existent wireless telecommunication service coverage. The information collection system collects information from the combine harvester 600 using any type of temporary ad hoc wireless connection when the combine harvester 600 is in proximity to the machine containing the information collection system, such as a fuel truck, before the combine harvester 600 is refueled. The collected information can then be forwarded to another network when the machine containing the received information reaches a location where wireless telecommunication service coverage or other wireless coverage is available. For example, the fuel truck can enter an area with wireless communication coverage when the fuel truck travels to a location to refuel other machines or at a main fuel storage location. All of these architectures are contemplated herein. Further, information can be stored on the agricultural harvester 600 until the agricultural harvester 600 enters an area with wireless communication coverage. The agricultural harvester 600 itself can transmit the information to another network.

[0239] It will also be noted that Figure 2The elements or portions thereof of the system can be provided on a variety of different devices. One or more of these devices can include an on-board computer, an electronic control unit, a display unit, a server, a desktop computer, a laptop computer, a tablet computer, or other mobile device such as a palmtop computer, a cellular telephone, a smart phone, a multimedia player, a personal digital assistant, and the like.

[0240] In some examples, the remote server architecture 500 can include network security measures. Without limitation, these measures include encryption of data on storage devices, encryption of data sent between network nodes, authentication of personnel or processes accessing data, and the use of ledgers to record metadata, data, data transfers, data access, and data transformations. In some examples, the ledgers can be distributed and immutable (e.g., implemented as blockchains).

[0241] Figure 15 is a simplified block diagram of one illustrative example of a handheld or mobile computing device that can be used as a handheld device 16 of a user or customer in which the present system (or a portion thereof) can be deployed. For example, the mobile device can be deployed in an operator's cab of an agricultural harvester 100 for use in generating, processing, or displaying the graphs discussed above. Figures 16 to 17 is an example of a handheld or mobile device.

[0242] Figure 15 A general block diagram of components of a client device 16 is provided that can run Figure 2 Some of the components shown in FIG. 16, interact with them, or both. In the device 16, a communications link 13 is provided that allows the handheld device to communicate with other computing devices, and in some examples a channel for automatically receiving information (e.g., by scanning) is provided. Examples of the communications link 13 include allowing communication over 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.

[0243] In other examples, the application can be received on a removable Secure Digital (SD) card that is connected to an interface 15. The interface 15 and the communications link 13 are in communication with a processor 17 (which can also implement the processor or server from other figures) along a bus 19 that is also connected to a memory 21 and input / output (I / O) components 23, as well as a clock 25 and a location system 27.

[0244] In one example, I / O components 23 are provided to facilitate input and output operations. I / O components 23 of various examples of device 16 can include input components, such as buttons, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors, and output components, such as display devices, speakers, and / or printer ports. Other I / O components 23 can also be used.

[0245] Clock 25 illustratively includes a real-time clock component that outputs time and date. Illustratively, it can also provide timing functions for processor 17.

[0246] Position system 27 illustratively includes a component that outputs a current geographic position of device 16. This can include, for example, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. Position system 27 can also include, for example, mapping software or navigation software that generates desired maps, navigation routes, and other geographic functions.

[0247] Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data stores 37, communication drivers 39, and communication configuration settings 41. Memory 21 can include all types of tangible volatile and non-volatile computer-readable memory devices. Memory 21 can also include computer storage media (described below). Memory 21 stores computer readable instructions that, when executed by processor 17, cause processor to perform computer-implemented steps or functions according to the instructions. Processor 17 can also be activated by other components to facilitate their functions.

[0248] Figure 16 One example is shown in which device 16 is a tablet computer 600. In Figure 16 In this example, computer 601 is shown with a user interface display screen 602. Screen 602 can be a touch screen that receives input from a pen or stylus or a pen-enabled interface. Tablet computer brain 600 can also use an on-screen virtual keyboard. Of course, computer 601 can also be attached to a keyboard or other user input device, for example, through a suitable attachment structure such as a wireless link or a USB port. Computer 601 can also illustratively receive voice input.

[0249] Figure 17 Similar to Figure 16, except that the device is a smartphone 71. The smartphone 71 has a touch-sensitive display 73 that displays icons or widgets or other user input mechanisms 75. The mechanisms 75 can be used by a user to run applications, make calls, perform data transfer operations, etc. Generally speaking, the smartphone 71 builds on the mobile operating system and provides more advanced computing capability and connectivity than a feature phone.

[0250] Note that other forms of the device 16 are possible.

[0251] Figure 18 is one example of a computing environment in which elements of Figure 2 may be deployed. Referring to Figure 18 , an example system for implementing some embodiments includes a computing device in the form of a computer 810 programmed to operate as discussed above. The components of computer 810 can include, but are not limited to, a processing unit 820 (which can include a processor or server from the previous figures), a system memory 830, and a system bus 821 that couples various system components including the system memory to the processing unit 820. The system bus 821 can be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The memory and programs described with respect to Figure 2 may be deployed in corresponding portions of Figure 18 .

[0252] The computer 810 typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer 810 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media can comprise computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. It includes hardware storage media including both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer 810. Communication media can embody computer readable instructions, data structures, program modules or other data in a modulated data signal or carrier wave. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.

[0253] System memory 830 includes computer storage media in the form of volatile and / or non-volatile memory or both, such as read-only memory (ROM) 831 and random access memory (RAM) 832. The basic input / output system 833 (BIOS) (which contains basic routines such as those that help transfer information between components within computer 810 during startup) is typically stored in ROM 831. RAM 832 typically contains data and / or program modules, or both, that are readily accessible to and / or currently being operated by processing unit 820. This is by way of example and not limitation. Figure 18 The operating system 834, application program 835, other program modules 836, and program data 837 are shown.

[0254] Computer 810 may also include other removable / non-removable volatile / non-volatile computer storage media. This is just one example. Figure 18 A hard disk drive 841 is shown that reads from or writes to a non-removable, non-volatile magnetic medium, an optical disk drive 855, and a non-volatile optical disk 856. The hard disk drive 841 is typically connected to the system bus 821 via a non-removable memory interface (such as interface 840), and the optical disk drive 855 is typically connected to the system bus 821 via a removable memory interface (such as interface 850).

[0255] Alternatively or additionally, the functions described herein may be performed at least in part by one or more hardware logic components. For example, but not limited to, illustrative types of hardware logic components that may be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (e.g., ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), and the like.

[0256] The above discussion and Figure 18 The driver and its associated computer storage medium shown provide the computer 810 with storage for computer-readable instructions, data structures, program modules, and other data. For example, in Figure 18In 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.

[0257] Users can input commands and information into computer 810 using 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 coupled to the system bus, but may also be connected via other interfaces and bus structures. Visual display 891 or other types of display devices are also connected to system bus 821 via an interface such as video interface 890. In addition to the monitor, the computer may also include other peripheral output devices, such as speakers 897 and printers 896, which can be connected via peripheral output interface 895.

[0258] Computer 810 operates in a networked environment using logical connections (such as Controller Area Network (CAN), Local Area Network (LAN), or Wide Area Network (WAN)) to one or more remote computers (such as remote computer 880).

[0259] 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 18 This demonstrates that remote application 885 can reside on remote computer 880.

[0260] It should also be noted that the different examples described in this article can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of this is considered in this article.

[0261] Example 1 is an agricultural operating machine, comprising:

[0262] A communication system that receives a map, the map including agricultural characteristics corresponding to different geographical locations in the field;

[0263] A geolocation sensor that detects the geolocation of the agricultural machinery;

[0264] a field sensor that detects a value of a crop component corresponding to the geographic location;

[0265] a predictive model generator that generates a predictive agricultural model based on the value of the agricultural property in the graph at the geographic location and the value of the crop component corresponding to the geographic location detected by the field sensor, the predictive agricultural model modeling a relationship between the agricultural property and the crop component; and

[0266] a predictive map generator that generates a functional predictive agricultural map of the field based on the values of the agricultural property in the graph and based on the predictive agricultural model, the functional predictive agricultural map mapping predicted values of crop components to the different geographic locations in the field.

[0267] Example 2 is the agricultural work machine of any or all preceding examples, wherein the predictive map generator configures the functional predictive agricultural map for use by a control system, the control system generating control signals based on the functional predictive agricultural map to control controllable subsystems on the agricultural work machine.

[0268] Example 3 is the agricultural work machine of any or all preceding examples, wherein the control signals control the controllable subsystems to control a speed of a cleaning fan on the agricultural work machine.

[0269] Example 4 is the agricultural work machine of any or all preceding examples, wherein the control signals control the controllable subsystems to control a size of an opening on a chaffer on the agricultural work machine.

[0270] Example 5 is the agricultural work machine of any or all preceding examples, wherein the graph includes a prior vegetation index map including vegetation index values corresponding to the different geographic locations in the field as values of the agricultural property.

[0271] Example 6 is the agricultural work machine of any or all preceding examples, wherein the predictive model generator is configured to determine a relationship between the crop component and vegetation index values based on the value of the crop component corresponding to the geographic location detected by the field sensor and a vegetation index value at the geographic location in the vegetation index map, the predictive agricultural model configured to receive the vegetation index value as a model input and generate a predicted value of the crop component as a model output based on the determined relationship.

[0272] Example 7 is the agricultural work machine of any or all preceding examples, wherein the map comprises a historical crop composition map comprising historical values of crop composition corresponding to the different geographic locations in the field as values of the agricultural property.

[0273] Example 8 is the agricultural work machine of any or all preceding examples, wherein the predictive model generator is configured to determine a relationship between the crop composition and the historical values of the crop composition based on the values of the crop composition corresponding to the geographic locations detected by the field sensors and the historical values of the crop composition at the geographic locations in the historical crop composition map, the predictive agricultural model being configured to receive the historical values of the crop composition as model inputs and generate predicted values of the crop composition as model outputs based on the determined relationship.

[0274] Example 9 is the agricultural work machine of any or all preceding examples, wherein the map comprises a prediction map mapping predicted values of an agricultural property corresponding to the different geographic locations in the field as values of the agricultural property.

[0275] Example 10 is the agricultural work machine of any or all preceding examples, wherein the predictive model generator is configured to determine a relationship between the crop composition and the predicted values of the agricultural property based on the values of the crop composition corresponding to the geographic locations detected by the field sensors and the predicted agricultural property values at the geographic locations in the prediction map, the predictive agricultural model being configured to receive the predicted agricultural property values as model inputs and generate predicted values of the crop composition as model outputs based on the determined relationship.

[0276] Example 11 is the agricultural work machine of any or all preceding examples, wherein the map comprises a soil property map mapping values of a soil property corresponding to the different geographic locations in the field as values of the agricultural property.

[0277] Example 12 is the agricultural work machine of any or all preceding examples, wherein the predictive model generator is configured to determine a relationship between the crop composition and the soil property based on the values of the crop composition corresponding to the geographic locations detected by the field sensors and the values of the soil property at the geographic locations in the soil property map, the predictive agricultural model being configured to receive predicted values of the soil property as model inputs and generate predicted values of the crop composition as model outputs based on the determined relationship.

[0278] Example 13 is a computer-implemented method of generating a functional predictive agricultural map, comprising:

[0279] receiving, at an agricultural work machine, a map comprising values of an agricultural property corresponding to different geographic locations in a field;

[0280] detecting a geographic position of the agricultural work machine;

[0281] detecting, with an in-field sensor, a value of a crop component corresponding to the geographic position;

[0282] generating a predictive agriculture model that models a relationship between the agricultural characteristic and the crop component; and

[0283] controlling a predictive map generator to generate a functional predictive agriculture map of the field based on the values of the agricultural characteristic in the map and the predictive agriculture model, the functional predictive agriculture map mapping predicted values of the crop component to the different geographic positions in the field.

[0284] Example 14 is the computer-implemented method of any or all preceding examples and further comprising:

[0285] configuring a functional predictive agriculture map for a control system, the control system generating control signals based on the functional predictive agriculture map to control controllable subsystems on the agricultural work machine.

[0286] Example 15 is the computer-implemented method of any or all preceding examples, wherein receiving the prior information map comprises receiving a prior vegetation index map, the prior vegetation index map including values of a vegetation index corresponding to the different geographic positions in the field as values of the agricultural characteristic.

[0287] Example 16 is the computer-implemented method of any or all preceding examples, wherein generating the predictive agriculture model comprises:

[0288] determining a relationship between the vegetation index and the crop component based on the values of the crop component corresponding to the geographic positions and the vegetation index values at the geographic positions in the vegetation index map; and

[0289] controlling a predictive model generator to generate the predictive agriculture model that receives values of the vegetation index as model inputs and generates predicted values of the crop component as model outputs based on the determined relationship.

[0290] Example 17 is the computer-implemented method of any or all preceding examples, wherein receiving the prior information map comprises receiving a historical crop component map, the historical crop component map including historical values of the crop component corresponding to the different geographic positions in the field as the agricultural characteristic.

[0291] Example 18 is the computer-implemented method of any or all preceding examples, wherein generating the predictive agriculture model comprises:

[0292] determine a relationship between historical crop composition values and crop composition based on values of crop composition corresponding to geographic locations and historical values of crop composition at the geographic locations in a historical crop composition map; and

[0293] control a predictive model generator to generate a predictive agricultural model that receives historical values of crop composition as model input and generates predicted values of crop composition as model output based on the determined relationship.

[0294] Example 19 is the computer-implemented method of any or all preceding examples, further comprising:

[0295] control an operator interface mechanism to present a functional predictive agricultural map.

[0296] Example 20 is an agricultural work machine, comprising:

[0297] a communication system that receives a map indicating values of an agricultural property corresponding to different geographic locations in a field;

[0298] a geographic location sensor that detects a geographic location of the agricultural work machine;

[0299] a field sensor that detects values of crop composition corresponding to the geographic location;

[0300] a predictive model generator that generates a predictive crop composition model based on values of the agricultural property at the geographic location in the map and values of the crop composition corresponding to the geographic location detected by the field sensor, the predictive crop composition model modeling a relationship between the agricultural property and the crop composition; and

[0301] a predictive map generator that generates a functional predictive crop composition map of the field based on the values of the agricultural property in the map and based on the predictive crop composition model, the functional predictive crop composition map mapping predicted values of the crop composition to the different geographic locations in the field.

[0302] Although the subject matter has been described in language specific to structural features or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. An agricultural work machine (100), comprising: a communication system (206) that receives a map, the map comprising values of an agricultural property corresponding to different geographic locations in a field; a geographic location sensor (204) that detects a geographic location of the agricultural work machine; a field sensor (208) that detects a value of a crop constituent corresponding to the geographic location; a predictive model generator (210) that generates a predictive agricultural model based on the value of the agricultural property at the geographic location in the map and the value of the crop constituent corresponding to the geographic location detected by the field sensor, the predictive agricultural model modeling a relationship between the agricultural property and the crop constituent; and a predictive map generator (212) that generates a functional predictive agricultural map of the field based on the values of the agricultural property in the map and based on the predictive agricultural model, the functional predictive agricultural map mapping predicted values of a crop constituent to the different geographic locations in the field.

2. The agricultural work machine of claim 1, wherein, The predictive map generator configures the functional predictive agricultural map for use by a control system, the control system generating control signals based on the functional predictive agricultural map to control controllable subsystems on the agricultural work machine.

3. The agricultural work machine of claim 2, wherein, The control signals control the controllable subsystems to control a speed of a cleaning fan on the agricultural work machine.

4. The agricultural work machine of claim 2, wherein, The control signals control the controllable subsystems to control a size of an opening on a grain sieve on the agricultural work machine.

5. The agricultural work machine of claim 1, wherein, The map comprises a prior vegetation index map, the prior vegetation index map comprising vegetation index values corresponding to the different geographic locations in the field as values of the agricultural property.

6. The agricultural work machine of claim 5, wherein, The predictive model generator is configured to determine a relationship between the crop constituent and vegetation index values based on the value of the crop constituent corresponding to the geographic location detected by the field sensor and a vegetation index value at the geographic location in the vegetation index map, the predictive agricultural model configured to receive the vegetation index value as a model input and generate a predicted value of the crop constituent as a model output based on the determined relationship.

7. The agricultural work machine of claim 1, wherein, The map comprises a historical crop constituent map, the historical crop constituent map comprising historical values of the crop constituent corresponding to the different geographic locations in the field as values of the agricultural property.

8. The agricultural work machine of claim 7, wherein, The predictive model generator is configured to determine a relationship between the crop constituent and historical values of the crop constituent based on the value of the crop constituent corresponding to the geographic location detected by the field sensor and a historical value of the crop constituent at the geographic location in the historical crop constituent map, the predictive agricultural model configured to receive the historical value of the crop constituent as a model input and generate a predicted value of the crop constituent as a model output based on the determined relationship.

9. A computer-implemented method of generating a functional predictive agricultural map, comprising: receiving a graph (258) at an agricultural work machine (100), the graph (258) comprising values of an agricultural property corresponding to different geographic locations in a field; detecting a geographic location of the agricultural work machine; detecting, with an in-field sensor (208), a value of a crop constituent corresponding to the geographic location; generating a predictive agricultural model that models a relationship between the agricultural property and the crop constituent; and controlling a predictive graph generator (212) to generate a functional predictive agricultural graph of the field based on the values of the agricultural property in the graph and the predictive agricultural model, the functional predictive agricultural graph mapping predicted values of the crop constituent to the different geographic locations in the field.

10. An agricultural work machine (100) comprising: a communication system (206) that receives a graph indicating values of an agricultural property corresponding to different geographic locations in a field; a geographic location sensor (204) that detects a geographic location of the agricultural work machine; an in-field sensor (208) that detects a value of a crop constituent corresponding to the geographic location; a predictive model generator (210) that generates a predictive crop constituent model based on the values of the agricultural property in the graph at the geographic location and the value of the crop constituent corresponding to the geographic location detected by the in-field sensor, the predictive crop constituent model modeling a relationship between the agricultural property and the crop constituent; and a predictive graph generator (212) that generates a functional predictive crop constituent graph of the field based on the values of the agricultural property in the graph and based on the predictive crop constituent model, the functional predictive crop constituent graph mapping predicted values of the crop constituent to the different geographic locations in the field.

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