Crop humidity map generator and control system

Abstract

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural property values at different geographic locations of a field. An on-board sensor on the agricultural work machine senses an agricultural property as the agricultural work machine moves through the field. A prediction map generator generates a prediction map that predicts a predicted agricultural property at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural property sensed by the on-board sensor. The prediction map 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] Various 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 aid in determining the scope of the subject matter claimed. SUMMARY

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

[0006] This summary is provided to introduce a selection of concepts, none of which are necessarily key or essential to the claimed subject matter. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background. BRIEF DESCRIPTION OF DRAWINGS

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

[0008] Figure 2 is a block diagram showing some parts of an agricultural harvester in more detail according to some examples of the present disclosure.

[0009] Figures 3A-3B (Hereinafter referred to collectively as Figure 3) shows a flowchart illustrating an example of the operation of an agricultural harvester in generating a map.

[0010] Figure 4is a block diagram illustrating one example of a prediction model generator and a prediction map generator.

[0011] Figure 5 is a flowchart of a process for an agricultural harvester to receive maps, detect field characteristics, and generate functional prediction maps for presentation or use to control the harvester during a harvesting operation or both.

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

[0013] Figures 7-9 illustrates one example of a mobile device that can be used in an agricultural harvester.

[0014] Figure 10 is a block diagram illustrating one example of a computing environment that can be used in an agricultural harvester and one example of the architecture illustrated in the preceding figures. DETAILED DESCRIPTION

[0015] 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. Alterations and further modifications of the described devices, systems, methods, and any further applications of the principles of the present disclosure are fully contemplated as would occur to one ordinarily skilled in the art to which the disclosure pertains. In particular, it is fully contemplated that the features, components, steps, or combinations thereof described with respect to one example can be combined with the features, components, steps, or combinations thereof described with respect to other examples of the present disclosure.

[0016] This specification relates to using in-field data acquired contemporaneously with agricultural operations in combination with prior data to generate functional prediction maps, and more particularly, functional prediction crop moisture maps. In some examples, the functional prediction crop moisture maps can be used to control an agricultural work machine (e.g., an agricultural harvester). Unless the machine settings also change, the performance of the agricultural harvester can degrade when the agricultural harvester enters an area of changing crop moisture. For example, in an area of decreasing crop moisture, the agricultural harvester can move quickly over the ground and move material through the machine at an increased feed rate. When an area of increasing crop moisture is encountered, the speed of the agricultural harvester over the ground can decrease, thereby decreasing the feed rate of the agricultural harvester, or the agricultural harvester can jam, lose grain, or face other issues. For example, an area of a field having increased crop moisture can have crop plants of a different physical structure compared to an area of the field having decreased crop moisture. For example, in an area of increasing crop moisture, some plant beads can have thicker stems, wider leaves, larger or more heads, etc. In other examples, an area of a field having increased crop moisture can have crop plant beads of greater biomass value due to the increased moisture content of the crop plant beads increasing the mass of the crop plant. These changes in plant bead structure in areas of changing crop moisture can also cause changes in the performance of the agricultural harvester as the agricultural harvester traverses the areas of changing crop moisture.

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

[0018] Vegetation index maps can thus be used to determine the presence and location of vegetation. In some examples, the vegetation index maps enable determination 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 a growing season, when crops are in a growing state, the vegetation index can show the progress of crop development. Thus, if a vegetation index map is generated early in the growing season or mid-way through the growing season, the vegetation index map can indicate the progress of development of the crop plants. For example, the vegetation index map can indicate whether the plants are underdeveloped, whether an adequate canopy has been established, or other plant attributes of plant development.

[0019] A topography map illustratively maps the elevation of the ground at different geographic locations in a field of interest. Since ground slope indicates changes in elevation, having two or more elevation values allows for the calculation of slope across an area having multiple known elevation values. By having more areas with known elevation values, greater slope interval sizes can be achieved. As an agricultural harvester traverses the topography in a known direction, the pitch and roll of the agricultural harvester can be determined from the slope of the ground (i.e., the areas of elevation change). When referred to below, topography characteristics can include, but are not limited to, elevation, slope (e.g., including machine direction relative to slope), and ground contour (e.g., roughness).

[0020] A soil property map illustratively maps soil property values (which can indicate soil type, soil moisture, soil coverage, soil structure, and various other soil properties) at different geographic locations in a field of interest. Thus, a soil property map provides geographically referenced 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 loam, clay loam, silt loam, peat loam, chalk loam, loam, and various other soil types. Soil moisture can refer to the amount of water held or otherwise contained in the soil. Soil moisture can also be referred to as soil wetness. Soil coverage can refer to the amount of an item or material covering the 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 residual crop (e.g., amount of plant stalks remaining) as well as 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), as well as various other descriptions. These are just examples. Various other characteristics and properties of soil can be mapped as soil property values on a soil property map.

[0021] The soil property maps can be generated based on data collected during another operation of the field of interest (e.g., a previous agricultural operation of the same season, such as a planting operation or a spraying operation, and a previous agricultural operation performed in a past season, such as a previous harvesting operation). The agricultural machines performing these agricultural operations can have on-board sensors that detect characteristics indicative of soil properties, such as characteristics indicative of soil type, soil moisture, soil coverage, soil structure, and various other characteristics indicative of various other soil properties. In addition, operational characteristics of the agricultural machines during the previous operations, machine settings or machine performance characteristics, and other data can be used to generate the soil property maps. For example, header height data indicative of the height of a header of an agricultural harvester at different geographic locations in the field of interest during a previous harvesting operation, and weather data indicative of weather conditions (e.g., precipitation data or wind data for an intervening time period, such as a time period since the previous harvesting operation and the generation of the soil property map) can be used to generate a soil moisture map. For example, by knowing the height of the header, the amount of residual plant residue (e.g., crop stalks) can be known or estimated, and can be used in conjunction with the precipitation data to predict the soil moisture level. This is just one example.

[0022] In other examples, surveys of the field of interest can be performed by various machines having sensors (e.g., imaging systems) or by people. Data collected during these surveys can be used to generate the soil property maps. For example, an aerial survey of the field of interest can be performed in which imaging of the field is performed, and based on the image data, a soil property map can be generated. In another example, a person can enter the field with or without the aid of a device such as a sensor to collect various data or samples, and based on the data or samples, a soil property map of the field can be generated. For example, a person can collect core samples at different geographic locations of the field of interest. These core samples can be used to generate a soil property map of the field. In other examples, the soil property map can be based on user or operator input, such as input from a farm manager, which can provide various data collected or observed by the user or operator.

[0023] In addition, the soil property maps can be obtained from remote sources (e.g., third party service providers or government agencies, such as the Natural Resources Conservation Service (NRCS) of the United States Department of Agriculture (USDA), the United States Geological Survey (USGS), and from various other remote sources.

[0024] In some examples, the soil property maps can be derived from sensor readings of one or more electromagnetic radiation bands reflected by the soil (or surface of the field). Without limitation, these bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

[0025] Historical crop moisture maps illustratively map crop moisture values for different geographic locations in one or more fields of interest. These historical crop moisture maps are collected from past harvesting operations on one or more fields. The crop moisture maps can display crop moisture in crop moisture value units. One example of a crop moisture value unit includes a numerical value, such as a percentage. However, in other examples, the crop moisture value units can be expressed in various other ways, such as a level value, such as “high, medium, low” or “high, normal / expected / anticipated, low,” among various other expressions. In some examples, the historical crop moisture maps can be derived from sensor readings of one or more crop moisture sensors. Without limitation, these crop moisture sensors can include a capacitive sensor, a microwave sensor, or a conductivity sensor, among others. In some examples, the crop moisture sensors can utilize one or more electromagnetic radiation bands to detect crop moisture.

[0026] Accordingly, the present discussion is directed to examples in which the system receives one or more of a historical crop moisture map, a vegetation index map, a topography map, a soil property map, or a map generated during a previous operation for a field, and also uses in-field sensors to detect characteristics that are indicative of crop moisture during a harvesting operation. The system generates a model from one or more of the received maps and in-field data from the in-field sensors, which models relationships between historical crop moisture values, vegetation index values, topography characteristic values, or soil property values. This model is used to generate a functional predictive crop moisture map that predicts crop moisture in a field. The functional predictive crop moisture map generated during a harvesting operation can be presented to an operator or other user or used to automatically control an agricultural harvester during a harvesting operation, or both. In some examples, the maps received by the system map values for characteristics other than crop moisture (e.g., “non-crop moisture values”), such as vegetation index values, topography characteristic values, or soil property values. In some examples, the maps received by the system map historical values for crop moisture.

[0027] Figure 1is a partially diagrammatic and partially schematic illustration 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 is also applicable to other types of harvesters, such as cotton harvesters, sugarcane harvesters, self-propelled forage harvester, swathers, or other agricultural work machines. Thus, the present disclosure is intended to encompass the various different types of harvesters described, and is therefore not limited to combine harvesters. Further, the present disclosure is directed 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 predictive maps can be applicable. Thus, the present disclosure is intended to encompass these various different types of harvesters and other work machines, and is therefore not limited to combine harvesters.

[0028] As shown, Figure 1 The agricultural harvester 100 exemplarily includes an operator cab 101, which can have various 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 indicated generally at 104. The agricultural harvester 100 also includes a feeder housing 106, a feeder accelerator 108, and a threshing machine indicated generally 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 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.

[0029] 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, the cleaning subsystem 118) that includes a cleaning fan 120, a chaffer 122, and a sieve 124. The material handling subsystem 125 also includes an outfeed beater 126, a residue elevator 128, a clean grain elevator 130, and an unloading auger 134 and 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. that are not shown in FIG. 1. Figure 1

[0030] In operation, as an overview, the agricultural harvester 100 illustratively moves through a field in the direction indicated by arrow 147. As the agricultural harvester 100 moves, the header 102 (and associated drapers 164) engage the crop to be harvested and collect 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 more detail below) that controls the actuators 107. The control system can also receive settings from the operator for establishing 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 independently of the others. 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, a tilt angle error and a roll angle error) with a responsiveness that is 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.​

[0031] Returning to the description of the operation of the agricultural harvester 100, after the crop is cut by the cutter 104, the severed 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 material is threshed by the cylinder 112 rotating the crop against the concave 114. The threshed crop is moved by the separator cylinder in the separator 116, where the discharge beater 126 moves a portion of the residue toward the residue subsystem 138. 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 in a pile from the agricultural harvester 100. 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.

[0032] 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 to a screw conveyor that moves the clean grain to an 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. An airflow generated by the cleaning fan 120 removes 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.

[0033] The chaffer elevator 128 returns the chaff to the threshing machine 110, where the chaff is re-threshed. Alternatively, the chaff can also be conveyed by the chaffer elevator or another transport device to a separate re-threshing mechanism, where the chaff is also re-threshed.

[0034] 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 view 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 set in the cleaning subsystem 118.

[0035] The ground speed sensor 146 senses the speed at which the agricultural harvester 100 is traveling 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 a ground engaging component (e.g., a wheel or track), a drive shaft, an axle, or other component. In some cases, travel speed can be sensed using a positioning system, such as a global positioning system (GPS), a dead reckoning system, a long-range navigation (LORAN) system, or a variety of other systems or sensors that provide an indication of travel speed.

[0036] The loss sensors 152 illustratively provide output signals indicating 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 grain impacts per unit time or per unit distance traveled to provide an indication of grain loss occurring in 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, the sensors 152 can include a single sensor, as opposed to providing separate sensors for each cleaning subsystem 118.

[0037] The splitter loss sensor 148 provides an indication of the left and right splitters ( Figure 1 The separator loss sensor 148 may be associated with the left and right separators and may provide separate grain loss signals or a combined or aggregated signal. In some cases, various types of sensors may be used to sense grain loss in the separators.

[0038] The agricultural harvester 100 can also include other sensors and measuring 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 (amplitude) of the agricultural harvester 100; a residue setting sensor configured to sense whether the agricultural harvester 100 is configured to chop residue, windrow, etc.; 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 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, and other crop properties. The crop property sensor can also be configured to sense properties of cut crop material when the agricultural harvester 100 is processing the crop material. For example, in some cases, the crop property sensor can sense: grain quality, such as broken grain, MOG levels; grain composition, 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 feed rate of biomass through the feeder housing 106, the separator 116, or elsewhere in the agricultural harvester 100. The crop property sensor can also sense the feed rate as 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 moisture sensors that sense a moisture of the crop being harvested by the agricultural harvester.

[0039] The crop moisture sensor can include a capacitive moisture sensor. In one example, the capacitive moisture sensor can include a moisture measurement cell for containing a sample of crop material and a capacitor for determining a dielectric property of the sample. In other examples, the crop moisture sensor can be a microwave sensor or a conductivity sensor. In other examples, the crop moisture sensor can use a wavelength of electromagnetic radiation to sense the moisture content of the crop material. The crop moisture sensor can be disposed within the feeder housing 106 (or otherwise have sensing access to the crop material within the feeder housing 106) and configured to sense the moisture of the harvested crop material passing through the feeder housing 106. In other examples, the crop moisture sensor can be located in other areas within the agricultural harvester 100, for example, in the clean grain elevator, in the clean grain auger, or in the grain tank. It should be noted that these are merely examples of crop moisture sensors, and various other crop moisture sensors are contemplated.

[0040] In some examples, crop moisture is the ratio of water to other plant material (e.g., dry matter of the grain) or total biomass. In other examples, crop moisture can relate to the amount of water on the outside of the plant, such as dew, frost, or rain. Crop moisture can be measured in absolute terms, such as the percentage of water to the mass or volume of the material. In other examples, crop moisture can be reported in relative categories, such as “high, medium, low,” “wet, typical / normal, dry,” and the like. Crop moisture can be measured in a variety of ways. In some examples, crop moisture can relate to the color of the crop (e.g., green) or the distribution of brown and green areas across the plant. In some examples, crop moisture can relate to the rate at which the color of the crop changes from green to brown during senescence. In other examples, crop moisture can be measured using properties in which electric fields or electromagnetic waves interact with water. Without limitation, these properties include dielectric constant, resonance, reflection, absorption, or transmission. In yet other examples, crop moisture can relate to the morphology of the plant (e.g., 3D leaf shape (e.g., corn leaf “laying”), leaf stomatal diameter affecting plant temperature, and relative stem diameter over time.

[0041] Before describing how the agricultural harvester 100 generates and uses a functional predicted crop moisture map for presentation or control, 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 drawing descriptions of the drawings depict 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 a characteristic in the field, such as a characteristic of a crop or weed 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 moisture, crop density, crop condition; characteristics of grain properties, such as grain moisture, grain size, grain test weight; and characteristics of machine performance, such as loss level, work quality, fuel consumption, and power utilization. A relationship between the characteristic values obtained from the in-field sensor signals and the prior information map values is determined, and the relationship is used to generate a new functional prediction map. The functional prediction map predicts values at different geographic locations in the field, and one or more of 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.

[0042] After describing general methods with reference to Figure 2 , Figure 3A and Figure 3B , more specific methods of generating a functional prediction crop moisture 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.

[0043] 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, a data storage device 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 concurrently 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 draper control controller 238, a belt conveyor belt controller 240, a deck 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.

[0044] Figure 2It is also shown that the agricultural harvester 100 can receive one or more prior information maps 258. As described below, for example, the one or more prior information maps include a vegetation index map in the field or a vegetation map, a topography map, or a soil property map from a prior or previous operation. 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 a prior or previous operation, for example, a historical crop moisture map from the past few years containing situational information related to historical crop moisture. The situational information can include, but is not limited to, one or more of weather conditions for the growing season, presence of pests, geographical location, soil type, irrigation, treatment applications, and the like. The weather conditions can include, but are not limited to, precipitation for the entire season, presence of hail that can damage the crop, presence of strong winds, seasonal temperatures, and the like. Some examples of pests broadly include insects, fungi, weeds, bacteria, viruses, and the like. Some examples of treatment 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, levers, 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 voice 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.

[0045] 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 download or transfer of information to and from a secure digital (SD) card or a universal serial bus (USB) card or both.

[0046] 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.

[0047] 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. The field data includes data acquired from sensors on the agricultural harvester or data acquired by any sensor in the case of detecting data during a harvesting operation.

[0048] After being retrieved by the agricultural harvester 100, the prior information map selector 209 can filter or select one or more particular prior information maps 258 for use by the prediction model generator 210. In one example, the prior information map selector 209 selects a map based on a comparison of situational information in the prior information map to current situational information. For example, a historical crop moisture map can be selected from a year in the past that has weather conditions similar to the current year’s weather conditions for the growing season. Or, for example, a historical crop moisture map can be selected from a year in the past when the situational information is not similar. For example, a historical crop moisture map for a previous year that was “dry” (i.e., drought conditions or reduced precipitation) can be selected when the current year is “wet” (i.e., increased precipitation or flood conditions). There can still be a useful historical relationship, but the relationship can be inverse. For example, dry areas in a dry year can be areas of higher crop moisture in a wet year because these areas can retain more moisture in wet years. The current situational information can include more than the most proximate situational 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 few years, etc.

[0049] Context information can also be used for correlation between areas with similar context characteristics, regardless of whether the geographic locations correspond to the same location on the prior information map 258. For example, context characteristic information associated with different locations can be applied to locations on the prior information map 258 with similar characteristic information.

[0050] The predictive model generator 210 generates a model indicative of a relationship between values sensed by the field sensors 208 and values mapped to the field by the prior information map 258. For example, if the prior information map 258 establishes a mapping of vegetation index values to different locations in the field, and the field sensors 208 are sensing values indicative of crop moisture, the prior information variable to field variable model generator 228 generates a predictive crop moisture model that models a relationship between vegetation index values and crop moisture values. The predictive map generator 212 then uses the predictive crop moisture model generated by the predictive model generator 210 to generate a functional predictive crop moisture map that predicts values of crop moisture 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 moisture values to different locations in the field, and the field sensors 208 are sensing values indicative of crop moisture, the prior information variable to field variable model generator 228 generates a predictive crop moisture model that models a relationship between historical crop moisture values (with or without context information) and field crop moisture values. The predictive map generator 212 then uses the predictive crop moisture model generated by the predictive model generator 210 to generate a functional predictive crop moisture map that predicts values of crop moisture at different locations in the field based on the prior information map 258. Or, for example, if the prior information map 258 maps terrain characteristic values to different locations in the field, and the field sensors 208 are sensing values indicative of crop moisture, the prior information variable to field variable model generator 228 generates a predictive crop moisture model that models a relationship between terrain characteristic values and crop moisture values. The predictive map generator 212 then uses the predictive crop moisture model generated by the predictive model generator 210 to generate a functional predictive crop moisture map that predicts values of crop moisture 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 to different locations in the field, and the field sensors 208 are sensing values indicative of crop moisture, the prior information variable to field variable model generator 228 generates a predictive crop moisture model that models a relationship between soil property values and crop moisture values. The predictive map generator 212 then uses the predictive crop moisture model generated by the predictive model generator 210 to generate a functional predictive crop moisture map that predicts values of crop moisture at different locations in the field based on the prior information map 258.

[0051] In some examples, the type of data in the functional prediction 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 prediction 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 prediction 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 dictate the type of data in the functional prediction map 263. In some examples, the type of data in the functional prediction 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 prediction 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 prediction 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 dictate the type of data in the functional prediction map 263. In some examples, the type of data in the functional prediction 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 prediction 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.

[0052] Continuing the foregoing example, the prediction map generator 212 can generate a functional prediction map 263 of crop moisture at different locations in the field using the values in the prior information map 258 and the models generated by the prediction model generator 210. The prediction map generator 212 thus outputs a prediction map 264.

[0053] As Figure 2As shown, the prediction map 264 is based on the prior information values in the prior information map 258 at those locations (or locations with similar situational information, even if in different fields) and uses a prediction model to predict values of the characteristic (which can be a characteristic sensed by one or more of the field sensors 208) or a characteristic related to the characteristic sensed by one or more of the field sensors 208 across multiple locations in the field. For example, if the prediction model generator 210 has generated a prediction model that indicates a relationship between vegetation index values and crop moisture, given vegetation index values at different locations across the field, the prediction map generator 212 generates a prediction map 264 that predicts values of crop moisture at different locations throughout the field. The prediction map 264 is generated using the vegetation index values at those locations obtained from the prior information map 258 and the relationship between vegetation index values and crop moisture obtained from the prediction model. Or, for example, if the prediction model generator 210 has generated a prediction model that indicates a relationship between historical crop moisture values and crop moisture, given historical crop moisture values at different locations throughout the field, the prediction map generator 212 generates a prediction map 264 that predicts values of crop moisture at different locations throughout the field. The historical crop moisture values at those locations obtained from the prior information map 258 and the relationship between historical crop moisture values and crop moisture obtained from the prediction model are used to generate the prediction map 264. Or, for example, if the prediction model generator 210 has generated a prediction model that indicates a relationship between terrain characteristic values and crop moisture, given terrain characteristic values at different locations throughout the field, the prediction map generator 212 generates a prediction map 264 that predicts values of crop moisture at different locations throughout the field. The terrain characteristic values at those locations obtained from the prior information map 258 and the relationship between terrain characteristic values and crop moisture obtained from the prediction model are used to generate the prediction map 264. Or, for example, if the prediction model generator 210 has generated a prediction model that indicates a relationship between soil property values and crop moisture, given soil property values at different locations throughout the field, the prediction map generator 212 generates a prediction map 264 that predicts values of crop moisture at different locations throughout the field. The soil property values at those locations obtained from the prior information map 258 and the relationship between soil property values and crop moisture obtained from the prediction model are used to generate the prediction map 264.

[0054] 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.

[0055] In some examples, the data type in the prior information map 258 is different from the data type sensed by the in-field sensor 208, and the data type in the prediction map 264 is the same as the data type sensed by the in-field sensor 208. For example, the prior information map 258 can be a vegetation index map, and the variable sensed by the in-field sensor 208 can be crop moisture. The prediction map 264 can then be a predicted crop moisture map that maps predicted crop moisture 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 in-field sensor 208 can be crop height. The prediction map 264 can then be a predicted crop height map that maps predicted crop height values to different geographic locations in the field.

[0056] Additionally, in some examples, the data type in the prior information map 258 is different from the data type sensed by the in-field sensor 208, and the data type in the prediction map 264 is different from both the data type in the prior information map 258 and the data type sensed by the in-field sensor 208. For example, the prior information map 258 can be a vegetation index map, and the variable sensed by the in-field sensor 208 can be crop moisture. 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 in-field sensor 208 can be yield. The prediction map 264 can then be a predicted speed map that maps predicted harvester speed values to different geographic locations in the field.

[0057] In some examples, the prior information map 258 is 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 sensor 208, and the data type in the prediction map 264 is the same as the data type sensed by the in-field sensor 208. For example, the prior information map 258 can be a topography map generated during planting, and the variable sensed by the in-field sensor 208 can be crop moisture. The prediction map 264 can then be a predicted crop moisture map that maps predicted crop moisture values to different geographic locations in the field.

[0058] In some examples, the prior information map 258 is 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 moisture map generated the previous year, and the variable sensed by the in-field sensors 208 can be crop moisture. As such, the prediction map 264 can be a predicted crop moisture map mapping predicted crop moisture values to different geographic locations in the field. In this example, the prediction model generator 210 can use the relative crop moisture differences in the georegistered prior information map 258 from the previous year to generate a prediction model modeling the relationship between the relative crop moisture differences on the prior information map 258 and the crop moisture values sensed by the in-field sensors 208 during the current harvesting operation. The prediction map generator 212 then uses the prediction model to generate the predicted yield map.

[0059] 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 region based on data values associated with the adjacent portions of the region in the prediction map 264 into one or more control zones. A control zone can include two or more contiguous portions of a region (e.g., a field) for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant. For example, the response time to change a setting of a controllable subsystem 216 can not be sufficient to respond desirably to a change in a value contained in a map such as the prediction map 264. In this case, the control zone generator 213 parses the map and identifies control zones of 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 adjustment. 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 a intercropping 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 determine the location and characteristics of two or more crops and then generate the prediction map 264 and the prediction control zone map 265 accordingly.

[0060] 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.

[0061] 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.

[0062] 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 moisture values displayed on the map based on the operator’s observations. The setting controller 232 can generate control signals to control various 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 one or more of, for example, the sieve and chaffer settings, the concave gap, the cylinder settings, the clean fan speed settings, the header height, the header functions, the reel speed, the reel position, the belt conveyor functions where the agricultural harvester 100 is coupled to a belt conveyor header, the grain header functions, the 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 turning subsystem 252 to turn 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 turning subsystem 252 to turn the agricultural harvester 100 along the route. The feed rate controller 236 can control various 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 crop moisture above a selected threshold, the feed rate controller 236 can reduce the speed of the agricultural harvester 100 to maintain a constant feed rate of grain or biomass through the agricultural harvester. 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 different types of seeds and weeds passing through the agricultural harvester 100, a particular type of machine cleanout operation or frequency of performing cleanout operations 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.

[0063] 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.

[0064] 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 the field, as indicated by block 282. For example, one prior information map can be a map generated during a previous operation, or generated based on data from a previous operation on the field (e.g., a previous spraying operation performed by a sprayer). The data for the prior information map 258 can be collected in various ways. For example, the data can be collected based on aerial images or measurements obtained the previous year or earlier in the current growing season or at other times. The information can also be based on data detected or collected in other ways (other than using aerial images). For example, the data for the prior information map 258 can be transmitted to the agricultural harvester 100 and stored in the data storage 202 using the communication system 206. The data for the prior information map 258 can also be provided to the agricultural harvester 100 in other ways using the communication system 206, as indicated by block 286 in the flowchart of FIG. 3. In some examples, the prior information map 258 can be received by the communication system 206.

[0065] 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 moisture 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, etc. The contextual information can be used to select which historical crop moisture map should be selected. For example, the weather conditions for a period of time (e.g., the weather conditions for the current year) or the soil properties of the current field can be compared to the 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 moisture map should be selected. For example, years with similar weather conditions can generally result in similar crop moisture or crop moisture trends across the field. In some cases, years with opposite weather conditions can also be helpful in predicting crop moisture from historical crop moisture. For example, areas with low crop moisture in dry years can have high crop moisture in wet years because the area can retain more moisture. 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 a different prior information map has 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.

[0066] At the start of a harvesting operation, the on-site sensors 208 generate sensor signals indicative of one or more on-site data values indicative of a plant characteristic such as moisture, 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 georeferenced using position, heading, or velocity data from the geolocation sensors 204.

[0067] The predictive 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.

[0068] The relationship or model generated by the predictive model generator 210 is provided to the predictive map generator 212. The predictive map generator 212 uses the predictive model and the prior information map 258 to generate a predictive 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.

[0069] 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 the map, maps different types of variables to geographic locations in the field. In such examples, the predictive model generator 210 generates predictive models that model the relationship between the field data and each of the different variables mapped by the two or more different maps or the two or more different layers of the map. Similarly, the field sensors 208 can include two or more sensors that each sense a different type of variable. Thus, the predictive model generator 210 generates predictive 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 predictive map generator 212 can use the predictive models and each of the maps or layers in the prior information map 258 to generate a functional predictive 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.

[0070] 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.

[0071] 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, the 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 one or more of 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 set values or control parameters based on the zones on the prediction control zone map 265 or the values on the prediction map 264 being used. 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 / authorization system can be provided to implement a verification and authorization process. For example, there can be a hierarchy of individuals that are authorized to view and change the information of the maps and other presentations. As an example, an onboard display device can display the maps in near real-time only 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 person 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 person 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 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 for use in 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.

[0072] 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 inputs from the geo-location sensor 204 that determine the geo-location of the agricultural harvester 100. Block 302 represents the control system 214 receiving sensor inputs that indicate 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.

[0073] 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 controlled can vary based on one or more different things. For example, the control signals generated and the controllable subsystems 216 controlled can vary based on the type of prediction map 264 or prediction control zone map 265, or both, that is being used. Similarly, the control signals generated, the controllable subsystems 216 controlled, and the timing of the control signals can vary based on the various delays of the crop flow through the agricultural harvester 100 and the responsiveness of the controllable subsystems 216.

[0074] As an example, the generated prediction map 264 in the form of a predicted crop moisture map can be used to control one or more controllable subsystems 216. For example, a functional predicted crop moisture map can include predicted values of crop moisture that are geographically registered to locations within the field being harvested. The functional predicted crop moisture 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 draw in 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 predicted values of crop moisture ahead of the machine 100, the predicted values of crop moisture over one portion of the header are greater than the predicted values of crop moisture over another portion of the header, resulting in different amounts of biomass entering one side of the header than the other side of the header, then 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 geographically registered predicted values in the predicted crop moisture map can be used to control the header and the swath controller 238 to control the belt conveyor speed of the belt conveyor on the header. The foregoing examples involving feed rate and header control using a functional predicted crop moisture map are provided by way of example only. Thus, a variety of other control signals can be generated using predicted values obtained from a predicted crop moisture map or other types of functional prediction maps to control one or more controllable subsystems 216.

[0075] At block 312, a determination is made as to whether the harvesting operation has been completed. If harvesting has not been completed, processing proceeds to block 314 in which field sensor data from the geolocation sensor 204 and the field sensors 208 (and possibly other sensors) is continually read.

[0076] 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.

[0077] 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 the 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 the 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, the new prediction map 264, the prediction control zone map 265, or both can be regenerated using the new prediction model. Block 318 represents detecting the threshold amount of field sensor data used to trigger the creation of a new prediction model.

[0078] In other examples, the learning trigger criteria can be based on how much the field sensor data from the field sensors 208 has changed, for example, over time or compared to previous or prior values. For example, if the change in the field sensor data (or the relationship between the field sensor data and the information in the prior information map 258) is within a selected range, 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 a new prediction map 264 and / or prediction control zone map 265. However, for example, if the change in the field sensor data is outside of the selected range, greater than the defined amount, or above the threshold, 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 a new prediction map 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 to cause the generation of a new prediction model and prediction map. Continuing 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 an automated system, or otherwise set.

[0079] Other learning trigger criteria can also be used. For example, if the prediction model generator 210 switches to a different prior information map (different from the initially selected prior information map 258), the switch 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 criterion.

[0080] 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.

[0081] 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 a manual adjustment to the controllable subsystem, which reflects that the operator 260 desires the controllable subsystem to operate differently than commanded by the control system 214. Thus, the operator 260 manually changing a setting can cause one or more of the following to be performed based on the adjustment made by the operator 260 (such as shown by block 322): causing the prediction model generator 210 to relearn a 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 using other trigger-based learning criteria.

[0082] In other examples, 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.

[0083] If relearning is triggered (whether based on a learning trigger criterion or based on an elapsed time interval), 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, as indicated by block 326. The new prediction models, the new prediction maps, and the new control algorithms are generated using any additional data collected since the last learning operation was performed. Performing relearning is indicated by block 328.

[0084] If the harvesting operation has been completed, the operation moves from block 312 to block 330, in which one or more of the predictive map 264, the predictive control zone map 265, and the predictive model generated by the predictive model generator 210 are stored. The predictive map 264, the predictive control zone map 265, and the predictive 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.

[0085] It will be noted that while some examples herein describe the predictive model generator 210 and the predictive map generator 212 receiving a priori information maps when generating a predictive model and a functional predictive map, respectively, in other examples, the predictive model generator 210 and the predictive map generator 212 can receive other types of maps when generating a predictive model and a functional predictive map, respectively, including predictive maps, such as functional predictive maps generated during a harvesting operation.

[0086] 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 An example of the predictive model generator 210 and the predictive map generator 212 is shown in more detail. Figure 4Information flow between the various different components shown therein is also illustrated. As shown, the predictive model generator 210 receives one or more of the vegetation index map 332, the historical crop moisture map 333, the topography map 341, the soil property map 343, or the prior operations map 400 as prior information maps. The historical crop moisture map 333 includes historical crop moisture values 335 that indicate crop moisture values for the entire field during the previous harvest. The historical crop moisture map 333 also includes scenario data 337 that indicates backgrounds or conditions that can have influenced the crop moisture values over the past year or past years. For example, the scenario data 337 can include soil properties (e.g., soil type, soil moisture, soil coverage, or soil structure), topographical characteristics (e.g., elevation or slope), planting date, harvest date, fertilization, seed type (hybrid, etc.), measure of weed presence, measure of pest presence, weather conditions (e.g., rainfall, snowfall, hail, wind, temperature), etc. The historical crop moisture map 333 can also include other items as shown by block 339. As shown in the example shown, the vegetation index map 332, the topography map 341, and the soil property map 343 do not contain additional information. However, in other examples, the vegetation index map 332, the topography map 341, the soil property map 343, and the prior operations map 400 can also include other items. For example, weed growth has an impact 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 topography map 341, or the soil property map 343.

[0087] In addition to receiving one or more of the vegetation index map 332, the historical crop moisture map 333, the topography map 341, the soil property map 343, or the prior operations map 400 as prior information maps, the predictive model generator 210 also receives an indication of a geographic location 334 or a geographic location from the geographic location sensor 204. The on-site sensors 208 illustratively include a crop moisture sensor 336 and a processing system 338. The processing system 338 processes sensor data generated by the crop moisture sensor 336. In some examples, the crop moisture sensor 336 can be on-board the agricultural harvester 100.

[0088] In some examples, the crop moisture sensor 336 can include a capacitive moisture sensor. In one example, the capacitive moisture sensor can include a moisture measurement cell for containing a sample of crop material and a capacitor for determining the dielectric properties of the sample. In other examples, the crop moisture sensor can be a microwave sensor or a conductivity sensor. In other examples, the crop moisture sensor can utilize the wavelength of electromagnetic radiation to sense the moisture content of the crop material. The crop moisture sensor can be disposed within the feeder housing 106 (or otherwise have sensing access to the crop material within the feeder housing 106) and configured to sense the moisture of the harvested crop material passing through the feeder housing 106. In other examples, the crop moisture sensor can be located at other areas within the agricultural harvester 100, for example, in the clean grain elevator, in the clean grain auger, or in the grain tank. It should be noted that these are merely examples of crop moisture sensors, and various other crop moisture sensors are contemplated. The processing system 338 processes one or more sensor signals generated by the crop moisture sensor 336 to generate processed sensor data that determines one or more crop moisture 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 moisture. This is because there is a period of time between the initial contact of the agricultural harvester with the crop plant and the crop plant material being sensed by the crop moisture sensor 336 or other field sensors 208. 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 moisture values can be georeferenced to a precise location on the field. Since the severed crop travels along the header in a direction transverse to the direction of travel of the agricultural harvester, the crop moisture values are generally geolocated to the scissor-shaped region behind the agricultural harvester as the agricultural harvester travels forward.

[0089] The processing system 338 allocates or apportions the total crop moisture detected by the crop moisture sensor during each time or measurement interval back to the earlier georeferenced region 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 allocates the measured total crop moisture from a measurement interval or time back to the georeferenced region traversed by the header of the agricultural harvester during the different measurement interval or time. The processing system 338 allocates or apportions the total crop moisture from a particular measurement interval or time to the previously traversed georeferenced region as part of the scissor-shaped region.

[0090] In some examples, crop moisture sensor 336 may rely on different types of radiation and how it is reflected, absorbed, attenuated, or transmitted by crop material. Crop moisture sensor 336 may sense other electromagnetic properties of the crop material, such as dielectric constant, as the material passes between two capacitive plates. Other material properties and sensors may also be used. In some examples, raw or processed data from crop moisture sensor 336 may be presented to operator 260 via operator interface mechanism 218. Operator 260 may be located onboard agricultural harvester 100 or at a remote location.

[0091] This discussion is directed to an example where the crop moisture sensor 336 detects a value indicative of crop moisture. It should be understood that this is merely one example, and that the aforementioned sensors are also contemplated herein as other examples of crop moisture sensors 336. Figure 4 As shown, the prediction model generator 210 includes a vegetation index to crop moisture model generator 342, a historical crop moisture to crop moisture model generator 344, a soil property to crop moisture model generator 345, a terrain characteristic to crop moisture model generator 346, and a priori operation to crop moisture model generator 348. In other examples, the prediction model generator 210 may include a vegetation index to crop moisture model generator 342, a historical crop moisture to crop moisture model generator 344, a soil property to crop moisture model generator 345, a terrain characteristic to crop moisture model generator 346, and a priori operation to crop moisture model generator 348. Figure 4 , or different components than those shown in the examples of FIG. 1 . Thus, in some examples, the prediction model generator 210 may also include other items 349, which may include other types of prediction model generators for generating other types of crop moisture models. For example, the other model generators 349 may include specific characteristics, such as specific vegetation index characteristics (e.g., crop growth or crop health), specific soil property characteristics (e.g., soil type, soil moisture, soil cover, or soil structure), or specific terrain characteristics (e.g., slope or elevation).

[0092] Vegetation index to crop moisture model generator 342 determines a relationship between the field crop moisture data 340 at the geographic location where the field crop moisture data 340 is georeferenced and the vegetation index value corresponding to the same location in the field where the field crop moisture data 340 is georeferenced from vegetation index map 332. Based on this relationship established by vegetation index to crop moisture model generator 342, vegetation index to crop moisture model generator 342 generates a predicted crop moisture model. Prediction map generator 212 uses the predicted crop moisture model to predict crop moisture at different locations in the field based on the georeferenced vegetation index values ​​at the same location in the field contained in vegetation index map 332.

[0093] The historical crop moisture to crop moisture model generator 344 determines a relationship between crop moisture at a geolocation corresponding to a location where the field crop moisture data 340 is geolocated and historical crop moisture at the same location (or a location with similar scenario data as the current region or year in the historical crop moisture map 333) in the historical crop moisture values 335. The historical crop moisture values 335 are values included in the historical crop moisture map 333 that are georeferenced and referenced in terms of scenario. The historical crop moisture to crop moisture model generator 344 then generates a predicted crop moisture model based on the historical crop moisture values 355 that the predicted crop moisture model is used by the predicted map generator 212 to predict crop moisture at a location in the field.

[0094] The soil property to crop moisture model generator 345 determines a relationship between the field crop moisture data 340 at a geolocation corresponding to a location where the field crop moisture data 340 is geolocated and soil property values from the soil property map 343 corresponding to the same location where the field crop moisture data 340 is geolocated in the field. Based on this relationship established by the soil property to crop moisture model generator 345, the soil property to crop moisture model generator 345 generates a predicted crop moisture model. The predicted crop moisture model is used by the predicted map generator 212 to predict crop moisture at a different location in the field based on georeferenced soil property values included in the soil property map 343 at the same location in the field.

[0095] The topographical characteristic to crop moisture model generator 346 determines a relationship between the field crop moisture data 340 at a geolocation corresponding to a location where the field crop moisture data 340 is geolocated and topographical characteristic values from the topographical map 341 corresponding to the same location where the field crop moisture data 340 is geolocated in the field. Based on this relationship established by the topographical characteristic to crop moisture model generator 346, the topographical characteristic to crop moisture model generator 346 generates a predicted crop moisture model. The predicted crop moisture model is used by the predicted map generator 212 to predict crop moisture at a different location in the field based on georeferenced topographical characteristic values included in the topographical map 341 at the same location in the field.

[0096] Prior operation-to-crop moisture model generator 348 determines a relationship between the field moisture data 340 at a geographic location corresponding to the location where the field moisture data 340 was geolocated and a priori operational characteristic values ​​from a priori operational map 400 corresponding to the same location in the field where the field moisture data 340 was geolocated. Based on this relationship established by a priori operational-to-crop moisture model generator 348, a priori operational-to-crop moisture model generator 348 generates a predicted crop moisture model. Predictive map generator 212 uses the predicted crop moisture model to predict crop moisture at different locations in the field based on the georeferenced a priori operational characteristic values ​​at the same location in the field included in a priori operational map 400.

[0097] In view of the above, the prediction model generator 210 is operable to generate a plurality of predicted crop moisture models, such as one or more of the predicted crop moisture models generated by the model generators 342, 344, 345, 346, 348, and 349. In another example, two or more of the above-mentioned predicted crop moisture models can be combined into a single predicted crop moisture model that predicts crop moisture based on vegetation index values, historical crop moisture values, soil property values, terrain characteristic values, or prior operating characteristic values, or both, at different locations in the field. Any one or combination of these crop moisture models is generated by Figure 4 The crop moisture model 350 is collectively represented.

[0098] The predicted crop moisture model 350 is provided to the prediction map generator 212. Figure 4 In the example of FIG. 3 , prediction map generator 212 includes a crop moisture map generator 352. In other examples, prediction map generator 212 may include additional, fewer, or different map generators. Crop moisture map generator 352 receives a predicted crop moisture model 350 that predicts crop moisture based on field data 340 and one or more of a vegetation index map 332, a historical crop moisture map 333, a topographic map 341, or a soil property map 343.

[0099] The crop moisture map generator 352 can generate a functional predictive crop moisture map 360 and a predictive crop moisture model 350 based on one or more of the vegetation index values at different locations in the field, historical crop moisture values, terrain characteristic values, or soil properties, said functional predictive crop moisture map predicting crop moisture at said locations in the field. The generated functional predictive crop moisture map 360 (with or without control zones) 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 predictive map, i.e., the predictive map 360, to produce a predictive control zone map 265. One or both of the functional predictive map 264 or the predictive control zone map 265 can be presented to the operator 260 or other user, or provided to the control system 214, said control system generating control signals to control one or more controllable subsystems 216 based on the predictive map 264, the predictive control zone map 265, or both.

[0100] Figure 5 is a flowchart of an example of the operations of the predictive model generator 210 and the predictive map generator 212 in generating a predictive crop moisture model 350 and a functional predictive crop moisture map 360. At block 362, the predictive model generator 210 and the predictive map generator 212 receive one or more prior vegetation index maps 332, one or more historical crop moisture maps 333, one or more prior terrain maps 341, one or more soil property maps 343, one or more prior operational maps 400, or a combination thereof. At block 362, receive field sensor signals from field sensors, e.g., crop moisture sensor signals from crop moisture sensors 336.

[0101] At block 363, the prior information map selector 209 selects one or more maps for use by the predictive model generator 210. In one example, the prior information map selector 209 selects one map from a plurality of candidate maps based on a comparison of situational information in the plurality of candidate maps to current situational information. For example, a candidate historical crop moisture map can be selected from a prior year in which the weather conditions of the growing season were similar to the weather conditions of the current year. Or, for example, a candidate historical crop moisture map can be selected from a prior year in which there was below average rainfall, while this year has average rainfall or above average rainfall, because the historical crop moisture associated with the prior year having below average rainfall can still have a useful historical crop moisture to crop moisture relationship, 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 field sensed crop moisture.

[0102] At block 372, the processing system 338 processes the one or more received sensor signals received from the field sensors 208 (e.g., one or more sensor signals received from the crop moisture sensor 336) to generate a crop moisture value indicative of the moisture of the harvested crop material.

[0103] At block 382, the predictive model generator 210 also obtains a geographic location corresponding to the sensor signal. For example, the predictive model generator 210 can obtain the geographic location from the geographic location sensor 204 and determine the exact geographic location to which the field sensed crop moisture is attributed based on machine latency (e.g., machine processing speed) and machine speed. For example, the exact time at which the crop moisture sensor signal is captured can not correspond to the time at which the crop is severed from the ground. Thus, the location of the agricultural harvester 100 when the crop moisture sensor signal is obtained can not correspond to the location of the planted crop. Rather, because a period of time elapses between the initial contact between the crop and the agricultural harvester and the crop reaching the crop moisture sensor 336, the current field crop moisture sensor signal corresponds to a location behind the agricultural harvester 100 on the field.

[0104] At block 384, the predictive model generator 210 generates one or more predictive crop moisture models, e.g., the crop moisture model 350, that model a relationship between at least one of a vegetation index value, a historical crop moisture value, a topographical characteristic value, or a soil property value obtained from the prior information map (e.g., the prior information map 258) and the crop moisture sensed by the field sensors 208. For example, the predictive model generator 210 can generate a predictive crop moisture model based on the vegetation index value, the historical crop moisture value, the topographical characteristic value, or the soil property value and the sensed crop moisture indicated by the sensor signal obtained from the field sensors 208.

[0105] At block 386, the predictive crop moisture model (e.g., the predictive crop moisture model 350) is provided to the predictive map generator 212, which generates a functional predictive moisture map that maps predicted crop moisture to different geographic locations in the field based on the vegetation index map, the historical crop moisture map 333, the topographical map 341, or the soil property map 343 and the predictive crop moisture model 350. For example, in some examples, the functional predictive crop moisture map 360 predicts crop moisture. In other examples, the functional predictive crop moisture map 360 predicts other items. Further, the functional predictive crop moisture map 360 can be generated during the course of an agricultural harvesting operation. Thus, the functional predictive crop moisture map 360 is generated as the agricultural harvester is moving through the field in which the agricultural harvesting operation is being performed.

[0106] At block 394, the prediction map generator 212 outputs the functional predicted crop moisture map 360. At block 393, the prediction map generator 212 configures the functional predicted crop moisture 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 generation and merging of control zones. At block 397, the prediction map generator 212 also configures the map 360 in other ways. The functional predicted crop moisture map 360 (with or without control zones) is provided to the control system 214. At block 396, the control system 214 generates control signals to control the controllable subsystems 216 based on the functional predicted crop moisture map 360 (with or without control zones).

[0107] The control system 214 can generate control signals to control a cutting table or other machine actuator 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 a threshing machine 110. 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 a communications system 206. The control system 214 can generate control signals to control an operator interface mechanism 218. The control system 214 can generate control signals to control various other controllable subsystems 256.

[0108] In examples where the control system 214 receives the functional prediction map or the functional prediction map with added 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. In examples where the control system 214 receives the functional prediction map or the functional prediction map with added 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 the functional prediction map or the functional prediction map with added control zones, the path planning controller 234 controls the steering subsystem 252 to cause the agricultural harvester 100 to steer. In another example, the control system 214 receives the functional prediction map or the functional prediction map with added control zones, the residue system controller 244 controls the residue subsystem 138. In another example, the control system 214 receives the functional prediction map or the functional prediction map with added control zones, the setting controller 232 controls the threshing machine settings of the threshing machine 110. In another example, the control system 214 receives the functional prediction map or the functional prediction map with added control zones, the setting controller 232 or another controller 246 controls the material handling subsystem 125. In another example, the control system 214 receives the functional prediction map or the functional prediction map with added control zones, the setting controller 232 controls the crop cleaning subsystem 118. In another example, the control system 214 receives the functional prediction map or the functional prediction map with added control zones, the machine cleaning controller 245 controls the machine cleaning subsystem 254 on the agricultural harvester 100. In another example, the control system 214 receives the functional prediction map or the functional prediction map with added control zones, the communication system controller 229 controls the communication system 206. In another example, the control system 214 receives the functional prediction map or the functional prediction map with added 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 the functional prediction map or the functional prediction map with added 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, the control system 214 receives the functional prediction map or the functional prediction map with added 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, the control system 214 receives the functional prediction map or the functional prediction map with added control zones, the other controller 246 controls other controllable subsystems 256 on the agricultural harvester 100.

[0109] As can be seen, the system employs a priori information map that maps characteristics such as vegetation index values, historical crop moisture values, terrain characteristic values, or soil property values to different locations in the field. The system also uses one or more in-field sensors that sense in-field sensor data indicative of a characteristic, such as crop moisture, and generates a model that models the relationship between the crop moisture sensed using the in-field sensor and the characteristic mapped in the a priori information map. Thus, the system uses the model and the a priori information 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 the combine harvester.

[0110] The current discussion has mentioned processors and servers. In some examples, processors and servers include computer processors with associated memory and timing circuitry (not shown separately), which can include both software and hardware aspects. Processors and servers are functional parts of the systems or devices of which they form a part, and functionally facilitate the operations of those systems.

[0111] 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, switch, 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.

[0112] 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 storage devices. In some examples, one or more of the data storage devices can be local to the system accessing the data storage devices, all of the data storage devices can be located remotely from the system utilizing the data storage devices, or one or more data storage devices can be local while others are remote. All of these configurations are contemplated by the present disclosure.

[0113] In addition, the figures show multiple blocks, wherein functionality is attributed to each block. It should be noted that fewer blocks can be used to illustrate that functionality attributed to multiple different blocks is performed by fewer components. Moreover, more blocks can be used to illustrate that functionality can be distributed among more components. In different examples, some functionality can be added, and some functionality can also be deleted.

[0114] It should be noted that the above discussion has described various different systems, components, logic, and interactions. It should be understood that any or all of such systems, components, logic, and interactions can be implemented by hardware items, such as processors, memories, or other processing components that perform functions associated with those systems, components, logic, or interactions, some of which are described below. In addition, any or all of the systems, components, logic, and interactions can be implemented by software that is loaded into a memory and then 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.

[0115] Figure 6 is a block diagram of an agricultural harvester 600, which may be similar to Figure 2 1. The agricultural harvester 100 shown in FIG. The agricultural harvester 600 communicates with the 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 the end user to know the physical location or configuration of the system delivering the services. In various examples, the remote server can use an appropriate protocol to deliver the services over a wide area network (such as the Internet). For example, the remote server can deliver the application over the wide area network and can be accessed through a web browser or any other computing component. Figure 2 The software or components shown in the and data associated therewith can be stored on a server at a remote location. The computing resources in a remote server environment can be consolidated at a remote data center location, or the computing resources can be dispersed across 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 for users. Therefore, 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.

[0116] existFigure 6 In the example shown in FIG. 2, some items are similar to Figure 2 the items shown in FIG. 1, and these items are similarly numbered. Figure 6 It is specifically shown that the prediction map generator 212 can be located at a server location 502 remote from the agricultural harvester 600. Thus, in Figure 6 the example shown in FIG. 2, the agricultural harvester 600 accesses the system through the remote server location 502.

[0117] Figure 6 Another example of a remote server architecture is also depicted. Figure 6 It is shown that Figure 2 Some of the elements of FIG. 2 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 through 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, where wireless telecommunication service coverage is poor or non-existent, another machine such as a fuel truck or other mobile machine or vehicle can have an automatic, semi-automatic, or manual information collection system. When the combine harvester 600 approaches a machine containing an information collection system, such as a fuel truck, before refueling, the information collection system collects information from the combine harvester 600 using any type of temporary ad hoc wireless connection. 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, when the fuel truck travels to a location where other machines are refueled or at a main fuel storage location, the fuel truck can enter an area with wireless communication coverage. 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.

[0118] 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.

[0119] 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 a ledger to record metadata, data, data transfers, data access, and data transformations. In some examples, the ledger can be distributed and immutable (e.g., implemented as a blockchain).

[0120] Figure 7 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 8 to 9 is an example of a handheld or mobile device.

[0121] Figure 7 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.

[0122] 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.

[0123] 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.

[0124] Clock 25 illustratively includes a real-time clock component that outputs time and date. It can also illustratively provide timing functions for processor 17.

[0125] 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.

[0126] 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.

[0127] Figure 8 An example is shown in which device 16 is a tablet computer 600. In Figure 8 In this example, computer 600 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 600 can also use an on-screen virtual keyboard. Of course, computer 600 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 600 can also illustratively receive voice input.

[0128] Figure 9 Similar to Figure 8, 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 is built on a mobile operating system and provides more advanced computing capability and connectivity than a feature phone.

[0129] Note that other forms of the device 16 are possible.

[0130] Figure 10 is one example of a computing environment in which elements of Figure 2 may be deployed. Referring to Figure 10 , an example system for implementing some embodiments includes a computing device in the form of a computer 810 programmed to operate as discussed above. The components of the 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. With Figure 2 the memory and programs described are deployable in the corresponding parts of Figure 10.

[0131] 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 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.

[0132] System memory 830 includes computer storage media in the form of volatile and / or nonvolatile memory such as read only memory (ROM) 831 and random access memory (RAM) 832. A basic input / output system 833 (BIOS), containing the basic routines that help to transfer information between elements within computer 810, such as during start-up, is typically stored in ROM 831. RAM 832 typically contains data and / or program modules that are immediately accessible to and / or presently being operated on by processing unit 820. By way of example, and not limitation, Figure 8 illustrates operating system 834, application programs 835, other program modules 836, and program data 837. Figure 10 Operating system 834, application programs 835, other program modules 836, and program data 837 are shown stored in system memory 830, for example.

[0133] Computer 810 can also include other removable / non-removable volatile / nonvolatile computer storage media. By way of example only, Figure 10A hard drive 841 is shown reading from and writing to a non-removable, non-volatile magnetic medium, an optical drive 855, and a non-volatile optical disk 856. The hard drive 841 is typically connected to the system bus 821 through a non-removable storage interface, such as interface 840, and the optical drive 855 is typically connected to the system bus 821 through a removable storage interface, such as interface 850.

[0134] Alternatively or additionally, the functions described herein may be at least partially performed by one or more hardware logic components. For example, but not limited to, exemplary 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), and complex programmable logic devices (CPLDs).

[0135] discussed above and in Figure 10 The drives and their associated computer storage media shown in FIG. 8 provide storage of computer readable instructions, data structures, program modules and other data for the computer 810. For example, in FIG. Figure 10 845, other program modules 846, and program data 847. Note that these components can be the same as or different from operating system 834, application programs 835, other program modules 836, and program data 837.

[0136] A user can enter commands and information into the computer 810 through input devices such as a keyboard 862, a microphone 863, and a pointing device 861, such as a mouse, trackball, or touch pad. Other input devices (not shown) may include a joystick, a game controller, a satellite dish, a scanner, or the like. These and other input devices are typically connected to the processing unit 820 through a user input interface 860 that is coupled to the system bus, but may be connected through other interface and bus structures. A visual display 891 or other type of display device is also connected to the system bus 821 through an interface such as a video interface 890. In addition to the monitor, the computer may also include other peripheral output devices, such as speakers 897 and a printer 896, which may be connected through an output peripheral interface 895.

[0137] The computer 810 operates in a networked environment using logical connections to one or more remote computers, such as a remote computer 880. The remote computer 880 can be a personal computer, a hand-held device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer 810, although only a memory storage device has been illustrated in FIG. 8. The logical connections depicted in FIG. 8 include a local area network (LAN) 871 and / or a wide area network (WAN) 873, such as the Internet. Such networking environments will be familiar to those skilled in the art. In an networked environment, the programs

[0138] When used in a LAN networking environment, the computer 810 is connected to the LAN 871 through a network interface or adapter 870. When used in a WAN networking environment, the computer 810 typically includes a modem 872 or other means for establishing communications over the WAN 873, such as the Internet. In a networked environment, program modules depicted relative to the computer 810, or portions thereof, can be stored in the remote memory storage device. It will be appreciated that the network connections shown are Figure 10 It is shown that a remote application 885 can reside on the remote computer 880.

[0139] It should also be noted that different examples described herein 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 possible combinations are considered here.

[0140] Another example is an example comprising any or all of the preceding examples, including:

[0141] A communication system receives a priori information map, the a priori information map comprising values of an agricultural property corresponding to different geographical locations in a field;

[0142] A geographical location sensor detects a geographical location of an agricultural work machine;

[0143] A field sensor detects a value of crop moisture corresponding to the geographical location;

[0144] A predictive model generator generates a predictive agricultural model based on the value of the agricultural property at the geographical location in the a priori information map and the value of the crop moisture corresponding to the geographical location detected by the field sensor, the predictive agricultural model modeling a relationship between the agricultural property and the crop moisture; and

[0145] A predictive map generator generates a functional predictive agricultural map of the field based on the values of the agricultural property in the a priori information map and based on the predictive agricultural model, the functional predictive agricultural map mapping predicted values of crop moisture to different geographical locations in the field.

[0146] Another example, including any or all of the preceding examples, wherein the predictive map generator configures a 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.

[0147] Another example, including any or all of the preceding examples, wherein the control signals control the controllable subsystems to adjust a rate of material feed through the agricultural work machine.

[0148] Another example, including any or all of the preceding examples, wherein the prior information map includes a prior vegetation index map, the prior vegetation index map including vegetation index values corresponding to different geographic locations in the field as values of the agricultural property.

[0149] Another example, including any or all of the preceding examples, wherein the predictive model generator is configured to determine a relationship between vegetation index values and crop moisture based on values of crop moisture detected by the field sensors corresponding to the geographic locations and vegetation index values in the vegetation index map at the geographic locations, the predictive agricultural model being configured to receive vegetation index values as model inputs and generate predicted values of crop moisture as model outputs based on the determined relationship.

[0150] Another example, including any or all of the preceding examples, wherein the prior information map includes a historical crop moisture map, the historical crop moisture map including historical values of crop moisture corresponding to different geographic locations in the field as values of the agricultural property.

[0151] Another example, including any or all of the preceding examples, wherein the predictive model generator is configured to determine a relationship between historical values of crop moisture and crop moisture based on values of crop moisture detected by the field sensors corresponding to the geographic locations and historical values of crop moisture in the historical crop moisture map at the geographic locations, the predictive agricultural model being configured to receive historical values of crop moisture as model inputs and generate predicted values of crop moisture as model outputs based on the identified relationship.

[0152] Another example, including any or all of the preceding examples, wherein the prior information map includes a topography map, the topography map including values of topographical properties corresponding to different geographic locations in the field as values of the agricultural property.

[0153] Another example, including any or all of the preceding examples, wherein the predictive model generator is configured to determine a relationship between the terrain characteristic and crop moisture based on a value of crop moisture corresponding to the geographic location detected by the field sensor and a value of the terrain characteristic in the topographic map at the geographic location, the predictive agricultural model configured to receive the value of the terrain characteristic as a model input and generate a predicted value of crop moisture as a model output based on the determined relationship.

[0154] Another example, including any or all of the preceding examples, wherein the prior information map comprises a soil property map, the soil property map comprising values of soil properties corresponding to different geographic locations in the field as values of the agricultural characteristic.

[0155] Another example, including any or all of the preceding examples, wherein the predictive model generator is configured to determine a relationship between the soil property and crop moisture based on a value of crop moisture corresponding to the geographic location detected by the field sensor and a value of the soil property in the soil property map at the geographic location, the predictive agricultural model configured to receive the value of the soil property as a model input and generate a predicted value of crop moisture as a model output based on the determined relationship.

[0156] Another example, including any or all of the preceding examples, comprising:

[0157] receiving, at an agricultural work machine, a prior information map, the prior information map indicating values of an agricultural characteristic corresponding to different geographic locations in a field;

[0158] detecting a geographic location of the agricultural work machine;

[0159] detecting, with a field sensor, a value of crop moisture corresponding to the geographic location;

[0160] generating a predictive agricultural model, the predictive agricultural model modeling a relationship between the agricultural characteristic and crop moisture; and

[0161] controlling a predictive map generator to generate a functional predictive agricultural map of the field based on the values of the agricultural characteristic in the prior information map and the predictive agricultural model, the functional predictive agricultural map mapping predicted values of crop moisture to different locations in the field.

[0162] Another example, including any or all of the preceding examples, and further comprising:

[0163] To configure a functional predictive agricultural map for a control system, the control system generates control signals based on the functional predictive agricultural map to control controllable subsystems on the agricultural work machine.

[0164] Another example, including any or all of the preceding examples, wherein receiving an a priori information map includes receiving a priori vegetation index map including vegetation index values corresponding to different geographic locations in a field as values of an agricultural characteristic.

[0165] Another example, including any or all of the preceding examples, wherein generating a predictive agricultural model includes:

[0166] determining a relationship between the vegetation index values and the crop moisture based on detected values of the crop moisture corresponding to the geographic locations and vegetation index values at the geographic locations in the vegetation index map; and

[0167] controlling a predictive model generator to generate a predictive agricultural model that receives vegetation index values as model inputs and generates predicted values of crop moisture as model outputs based on the determined relationship.

[0168] Another example, including any or all of the preceding examples, wherein receiving an a priori information map includes receiving a historical crop moisture map including historical values of crop moisture corresponding to different geographic locations in a field as values of the agricultural characteristic.

[0169] Another example, including any or all of the preceding examples, wherein generating a predictive agricultural model includes:

[0170] determining a relationship between the historical crop moisture and the crop moisture based on values of the crop moisture detected by the in-field sensors corresponding to the geographic locations and historical values of crop moisture at the geographic locations in the historical crop moisture map; and

[0171] controlling a predictive model generator to generate the predictive agricultural model that receives historical values of crop moisture as model inputs and generates predicted values of crop moisture as model outputs based on the determined relationship.

[0172] Another example, including any or all of the preceding examples, further including:

[0173] controlling an operator interface mechanism to present the functional predictive agricultural map.

[0174] Another example, including any or all of the preceding examples, including:

[0175] A communication system receives an a priori map indicating values of an agricultural property corresponding to different geographic locations in a field;

[0176] A geographic location sensor detects a geographic location of an agricultural work machine;

[0177] A field sensor detects a value of crop moisture corresponding to the geographic location;

[0178] A predictive model generator generates a predictive crop moisture model based on the value of the agricultural property at the geographic location in the a priori map and the value of crop moisture corresponding to the geographic location detected by the field sensor, the predictive crop moisture model modeling a relationship between the agricultural property and the crop moisture; and

[0179] A predictive map generator generates a functional predictive crop moisture map of the field based on the agricultural property value in the a priori map and based on the predictive crop moisture model, the functional predictive crop moisture map mapping predicted values of crop moisture to the different locations in the field.

[0180] Another example includes any or all of the preceding examples, wherein the a priori map includes one or more of:

[0181] A vegetation index map indicating vegetation index values corresponding to the different geographic locations in the field as values of the agricultural property;

[0182] A historical crop moisture map indicating historical values of crop moisture corresponding to the different geographic locations in the field as values of the agricultural property;

[0183] A topography map indicating values of topographical properties corresponding to the different geographic locations in the field as values of the agricultural property; or

[0184] A soil property map indicating values of soil properties corresponding to the different geographic locations in the field as values of the agricultural property. 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 system (100), comprising: a communication system (206) that receives a priori information map including values ​​of an agricultural characteristic corresponding to different geographical locations in a field; a geographic location sensor (204) for detecting a geographic location of the agricultural machine; a field sensor (208) that detects a value of crop moisture corresponding to the geographic location; a prediction model generator (210) for generating a prediction agricultural model based on the value of the agricultural characteristic at the geographical location in the prior information map and the value of the crop moisture corresponding to the geographical location detected by the field sensor, the prediction agricultural model modeling the relationship between the agricultural characteristic and the crop moisture; and A prediction map generator (212) generates a functional prediction agricultural map of the field based on the values ​​of the agricultural characteristics in the prior information map and based on the prediction agricultural model, wherein the functional prediction agricultural map maps the predicted values ​​of crop moisture to the different geographical locations in the field.

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

3. The agricultural system according to claim 2, wherein: The control signal controls the controllable subsystem to regulate a feed rate of material through the agricultural work machine.

4. The agricultural system according to claim 1, wherein: The a priori information map includes an a priori vegetation index map including vegetation index values ​​corresponding to the different geographical locations in the field as values ​​of the agricultural characteristics.

5. The agricultural system according to claim 4, wherein: The prediction model generator is configured to determine the relationship between the vegetation index value and the crop moisture based on the value of the crop moisture corresponding to the geographic location detected by the field sensor and the vegetation index value at the geographic location in the vegetation index map, and the prediction agricultural model is configured to receive the vegetation index value as a model input and generate a predicted value of crop moisture as a model output based on the determined relationship.

6. The agricultural system according to claim 1, wherein: The prior information map includes a historical crop moisture map including historical values ​​of crop moisture corresponding to the different geographical locations in the field as the values ​​of the agricultural characteristic.

7. The agricultural system according to claim 6, wherein: The prediction model generator is configured to determine the relationship between historical values ​​of crop moisture and crop moisture based on the value of crop moisture corresponding to the geographic location detected by the field sensor and the historical values ​​of crop moisture at the geographic location in the historical crop moisture map, and the prediction agricultural model is configured to receive the historical values ​​of crop moisture as model input and generate a predicted value of crop moisture as a model output based on the determined relationship.

8. The agricultural system according to claim 1, wherein: The priori information map includes a topographic map including, as the values ​​of the agricultural characteristics, values ​​of topographic characteristics corresponding to different geographical locations in the field.

9. A computer-implemented method for generating a functional predictive agricultural map, comprising: receiving a priori information map (258) indicating values ​​of an agricultural characteristic corresponding to different geographical locations in a field; Detecting the geographical location of the agricultural machine (100); Using a field sensor (208) to detect a value of crop moisture corresponding to the geographical location; generating a predictive agricultural model that models a relationship between the agricultural characteristic and crop moisture; and A prediction map generator (212) is controlled to generate a functional prediction agricultural map of the field based on the values ​​of the agricultural characteristics in the prior information map and the prediction agricultural model, the functional prediction agricultural map mapping the predicted values ​​of crop moisture to the different locations in the field.

10. An agricultural system (100), comprising: a communication system (206) that receives an a priori map indicating values ​​of an agricultural characteristic corresponding to different geographic locations in a field; a geographic location sensor (204) for detecting a geographic location of the agricultural machine; a field sensor (208) configured to detect a value of crop moisture corresponding to the geographic location; a prediction model generator (210) for generating a prediction crop moisture model based on the value of the agricultural characteristic at the geographic location in the a priori map and the value of the crop moisture corresponding to the geographic location detected by the field sensor, the prediction crop moisture model modeling a relationship between the agricultural characteristic and the crop moisture; and A prediction map generator (212) generates a functional prediction crop moisture map for the field based on the agricultural characteristic values ​​in the prior map and based on the prediction crop moisture model, the functional prediction crop moisture map mapping the predicted values ​​of crop moisture to the different geographical locations in the field.

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