Prediction map generation and control based on seeding characteristics

Abstract

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

Description

TECHNICAL FIELD

[0001] This description 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 fitted with different types of headers to harvest different types of crops.

[0003] Different types of agricultural machines can operate on fields that have various characteristics. Each of the different characteristics of the fields on which agricultural machines operate can vary across the field. Agricultural harvesters can operate differently in different areas of a field depending on the characteristics of those areas.

[0004] The discussion above is provided solely for the purpose of general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter. 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 characteristic values at different geographic locations of a field. An on-board sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A prediction map generator generates a prediction map that predicts a predicted agricultural characteristic at different locations in the field based on a relationship between values in the one or more information maps and the agricultural characteristic 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 that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. The claimed subject matter is not limited to addressing any or all of the disadvantages identified in the background. BRIEF DESCRIPTION OF DRAWINGS

[0007] Figure 1 is a partially pictorial, partially schematic diagram of portions of a combine harvester, which is one example of an agricultural machine.

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

[0009] Figures 3A-3Bis a flowchart showing an example of the operation of an agricultural harvester in generating a map.

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

[0011] Figure 4B is a block diagram showing an example of a field sensor.

[0012] Figure 5 is a flowchart showing an example of the operation of an agricultural harvester in receiving a planting map, detecting characteristics, and generating a function prediction map for use in controlling the agricultural harvester during a harvesting operation.

[0013] Figure 6 is a block diagram showing one example of an agricultural harvester in communication with a remote server environment.

[0014] Figures 7-9 shows an example of a mobile device that can be used in an agricultural harvester.

[0015] Figure 10 is a block diagram showing one example of a computing environment that can be used in an agricultural harvester and in the structures shown in the previous figures. DETAILED DESCRIPTION

[0016] To facilitate an understanding of the principles of the present disclosure, reference will now be made to the examples illustrated in the drawings, and described herein, and specific language will be used to describe the same. 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 additional or additional applications of the principles of the present disclosure are fully contemplated as would be apparent to one of ordinary skill in the art. In particular, it is fully contemplated that the features, components, steps, and / or combinations thereof described with respect to one example can be combined with the features, components, steps or combinations thereof of any other example described herein.

[0017] This specification relates to using in-field data acquired contemporaneously with agricultural operations to join previous data to generate a prediction map, and more particularly, a prediction property map that correlates in-field data with previous data to predict a property, or a related property, indicated by the in-field data across a field. In some examples, the prediction property map can be used to control an agricultural work machine, such as an agricultural harvester. Planting properties can vary across a field. Other agricultural properties, such as non-machine properties or machine properties, can be influenced by, or otherwise have a relationship with, various planting properties, such that various agricultural properties, such as non-machine properties or machine properties, can be predicted in different areas of a field having similar planting properties. For example, a yield or biomass of a crop in one area of a field having known (or estimated) planting properties can be similar to a yield or biomass of a crop in another area of a field having known (or estimated) similar planting properties. Yield and biomass are merely examples, and various other properties in different areas of a field can be predicted based on planting properties.

[0018] Performance of an agricultural machine can be influenced by agricultural properties, and as such, by predicting agricultural properties across a field, an agricultural machine can be controlled to optimize operation of the agricultural machine given the predicted agricultural properties. For example, by predicting a biomass of a crop across a field based on data from a planting map and in-field data indicative of biomass, such as crop height, crop density, crop mass, crop volume, or threshing rotor drive power, among other properties, a position of a header of an agricultural harvester relative to a surface of a field or an advance speed of the agricultural harvester can be adjusted to control a throughput or feed rate of plant material to be processed by the agricultural harvester. These are merely some examples.

[0019] Performance of an agricultural harvester can be influenced based on a number of different agricultural properties, such as non-machine properties (e.g., properties of a field or properties of plants on a field) or a number of different machine properties of the agricultural harvester, such as machine settings, operating properties, or properties of machine performance. Sensors on the agricultural harvester can be used in the field to detect these agricultural properties or to detect values indicative of these agricultural properties, and the agricultural harvester can be controlled in various ways based on these agricultural properties or properties related to these agricultural properties detected by the in-field sensors.

[0020] Seeding maps illustratively map seeding characteristics across different geographic locations in a field of interest. These seeding maps are typically collected from past seed planting operations on a field. In some examples, seeding maps can be derived from control signals used by a planter while planting seeds, or from sensors such as sensors that confirm that seeds were delivered to furrows generated by the planter. Planters can include geographic position sensors that geolocate where seeds were planted and topographic sensors that generate topographic information for a field. For example, topographic sensors can include GPS, laser levels, inclinometer / odometer pairs, local radio triangulators, and various other systems for generating topographic information. Information generated during previous seed planting operations can be used to determine various seeding characteristics such as location (e.g., the geographic location of planted seeds in a field), spacing (e.g., spacing between individual seeds, spacing between seed rows, or both), population (which can be derived from spacing characteristics), seed orientation (e.g., seed orientation in a furrow or orientation of seed rows), depth (e.g., seed depth or furrow depth), size (such as seed size), or genotype (such as seed variety, seed hybrid, seed cultivar, genotype, etc.). Various other seeding characteristics can also be determined. In some examples, seeding maps can include information related to seedbeds in which seeds are placed, such as soil moisture, soil temperature, soil structure (such as soil organic matter).

[0021] Instead of or in addition to data from previous operations, various seeding characteristics on seeding maps can be generated based on data from third parties such as third party seed suppliers that provide seeds for seed plant operations. These third parties can provide various data indicative of various seeding characteristics such as size data (such as seed size) or genotype data (such as seed variety, seed hybrid, seed cultivar, or seed breeding). Additionally, seed suppliers can provide various data related to specific plant characteristics of resulting plants for each different seed genotype, for example, data related to plant growth (such as stalk diameter, ear size, plant height, plant mass, etc.), plant reaction to weather conditions, plant reaction to applied substances (such as herbicides, fungicides, pesticides, insecticides, fertilizers), shatter characteristics, dry down characteristics, crop reaction to weather, crop reaction to pests, and crop reaction to fungi, etc., plant reaction to pests, fungi, weeds, disease, etc., and any number of other plant characteristics. It should be noted that plant reaction data can include data indicative of plant resistance to various conditions and characteristics, for example, plant resistance to applied substances, plant resistance to weather conditions, plant resistance to pests, fungi, weeds, disease, etc., and plant resistance to various other conditions or characteristics.

[0022] Alternatively or in addition to data from previous operations or from third parties, various planting characteristics on the planting map can be generated based on various user or operator input data, such as user or operator input data indicative of various planting characteristics such as location, depth, orientation, spacing, size, genotype, and various other planting characteristics.

[0023] In some examples, the planting map can be derived from sensor readings of one or more electromagnetic radiation bands reflected by the seeds or seedbed. Without limitation, these electromagnetic radiation bands can be in the microwave, infrared, visible, or ultraviolet portion of the electromagnetic spectrum.

[0024] These are merely some examples of ways in which a planting map can be generated and provided in the current system. Those skilled in the art will appreciate that planting maps can be generated in various ways and the scope of the present disclosure is not limited to the examples provided herein.

[0025] In some examples, the planting characteristics provided by the planting map can have relationships with or otherwise influence various other characteristics. By knowing the planting characteristics across a field, various other characteristics across the field can be predicted. During a harvesting operation, on-board sensors on an agricultural harvester can be used to detect various characteristics of the operating environment of the agricultural harvester or various machine characteristics of the agricultural harvester. Characteristics sensed by the on-board sensors corresponding to one or more geographic locations of the field can be used with the georeferenced planting characteristics provided by the planting map to predict characteristics at other geographic locations across the field. For example, by knowing the seed population in one or more geographic locations of the field (as obtained from the planting map) and the resulting yield at those one or more geographic locations (as indicated by the on-board sensors), the yield in other geographic locations across the field (e.g., other geographic locations planted with the same seed population) can be predicted. The combination of seed population and yield is merely one example. Relationships between various planting characteristics and various characteristics sensed by the on-board sensors can be modeled to predict characteristics sensed by the on-board sensors across the field.

[0026] Accordingly, the present discussion continues with reference to a system that receives a planting map for a field or a map generated based on a prior operation, such as a prior seed plant operation, and uses in-field sensors to detect variables indicative of one or more characteristics during a harvesting operation, such as agricultural characteristics (e.g., non-machine characteristics, such as characteristics of the field or characteristics of plants on the field) and machine characteristics, such as machine settings, operational characteristics, or machine performance data. However, it will be noted that the in-field sensors can detect variables indicative of any of a number of characteristics and are not limited to the characteristics described herein. Agricultural characteristics are any of a number of characteristics that can affect an agricultural operation, such as a harvesting operation. The system generates a model that models a relationship between planting characteristic values on the planting map or values of a map generated from a prior operation and output values from the in-field sensors. The model is used to generate a functional prediction map that predicts characteristics at different locations in the field indicated by the output values from the in-field sensors. The functional prediction map generated during a harvesting operation can be presented to an operator or other user during the harvesting operation, used to automatically control the agricultural harvester, or both.

[0027] Figure 1 is a partially pictorial, partially schematic illustration of portions of a self-propelled agricultural harvester 100. In the illustrated example, the agricultural harvester 100 is a combine harvester. Additionally, while a combine harvester is provided as an example throughout this disclosure, it will be understood that the present description also applies to other types of harvesters, such as a cotton harvester, a sugarcane harvester, a self-propelled forage harvester, a swather, or other agricultural work machines. Accordingly, the present disclosure is intended to encompass the various types of harvesters described and is thus not limited to a combine harvester. Moreover, the present disclosure relates to other types of work machines that can be applicable to generating prediction maps, such as agricultural planters and sprayers, construction equipment, forestry equipment, and turf management equipment. Accordingly, the present disclosure is intended to encompass these various types of harvesters and other work machines and is thus not limited to a combine harvester.

[0028] As Figure 1As shown in FIG. 1, the agricultural harvester 100 illustratively includes an operator cab 101 that can have various different operator interface mechanisms to control the agricultural harvester 100. The agricultural harvester 100 includes a set of front end equipment such as a header 102 and a cutter 104. The agricultural harvester 100 also includes a feeder housing 106, a feeder accelerator 108, and a threshing machine generally indicated as 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 movement of the header 102 about the axis 105 in a direction generally indicated by arrow 109. As such, a vertical position of the header 102 above the ground 111 (header height) on which the header 102 travels is controllable by actuation of the actuators 107. Although Figure 1 Not shown in FIG. 1, but the agricultural harvester 100 can also include one or more actuators that operate to apply a tilt angle, a roll angle, or both a tilt angle and a roll angle to the header 102 or portions of the header 102. Tilt refers to an angle at which the cutter 104 engages the crop. For example, the tilt angle is increased by controlling the header 102 to point a distal edge 113 of the cutter 104 more toward the ground. The tilt angle is decreased by controlling the header 102 to point the distal edge 113 of the cutter 104 more away from the ground. Roll angle refers to an 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 rotor 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 grain cleaning subsystem or cleaner (collectively referred to as a grain cleaning subsystem 118) that includes a grain cleaning fan 120, a chaffer 122, and a sieve 124. The material handling subsystem 125 also includes a beater 126, a tailings elevator 128, a clean grain elevator 130, and an unloading auger 134 and a spout 136. The clean grain elevator moves clean grain into a clean grain tank 132. The agricultural harvester 100 also includes a residue subsystem 138 that can include a chopper 140 and a spreader 142. The combine 100 also includes a propulsion subsystem that includes an engine that drives ground engaging components 144 such as 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 mentioned above. In some examples, the agricultural harvester 100 can have left and right grain cleaning subsystems, separators, etc., that are not shown in Figure 1 ​

[0030] In operation, and by way of 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 reel 164) engages the crop to be harvested and gathers the crop toward the cutter 104. The operator of the agricultural harvester 100 can be a local human operator, a remote human operator, or an automated system. Operator commands are commands from the operator. The operator of the agricultural harvester 100 can determine one or more of 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 that controls the actuator 107, which is described in greater detail below. The control system can also receive settings from the operator to establish the tilt angle and roll angle of the header 102 and implement the input settings by controlling the associated actuators (not shown) that change the tilt angle and roll angle of the header 102. The actuator 107 maintains the header 102 at a height above the ground 111 based on the height setting, and, where applicable, at the 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 examples, tilt angle errors and roll angle errors) with a responsiveness determined based on a selected sensitivity level. If the sensitivity level is set to a higher 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 cut crop material moves through the feeder housing 106 by a conveyor toward the feed accelerator 108, which accelerates the crop material into the threshing machine 110. The crop material is threshed by the crop being turned against the concave 114 by the threshing rotor 112. The threshed crop material moves through a separator rotor in the separator 116, with a portion of the residue moving through the unloading beater 126 toward the residue subsystem 138. The residue portion passed to the residue subsystem 138 is chopped by the residue chopper 140 and spread on the field by the spreader 142. In other configurations, the residue is discharged from the agricultural harvester 100 in a pile. In other examples, the residue subsystem 138 can include a weed seed eliminator (not shown), such as a seed bagger or other seed collector, or a seed crusher or other seed destroyer.

[0032] The grain falls to the clean grain subsystem 118. The chaffer 122 separates some of the larger pieces of material from the grain, and the screen 124 separates some of the finer pieces of material from the clean grain. The clean grain falls to a screw conveyor that moves the clean grain to the inlet end of the clean grain elevator 130, and the clean grain elevator 130 moves the clean grain upward, depositing the clean grain in the clean grain bin 132. Residue is removed from the clean grain subsystem 118 by the airflow generated by the clean grain fan 120. The clean grain fan 120 directs air up through the screen and the chaffer along an airflow path. The airflow carries residue back in the agricultural harvester 100 toward the residue handling subsystem 138.

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

[0034] Figure 1 It is also shown that, in one example, the agricultural harvester 100 includes a ground speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward-looking image capture mechanism 151 (which can be in the form of a stereo camera or a monocular camera), and one or more loss sensors 152 disposed in the clean grain subsystem 118.

[0035] The ground speed sensor 146 senses the speed of travel of the agricultural harvester 100 over the ground. The ground speed sensor 146 can sense the speed of travel of the agricultural harvester 100 by sensing the rotational speed of ground-engaging components such as wheels or tracks, drive shafts, axles, or other components. In some cases, the speed of travel can also 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 wide variety of other systems or sensors that provide an indication of the speed of travel.

[0036] The loss sensors 152 illustratively provide output signals indicative of the amount of grain loss occurring on the right and left sides of the clean grain subsystem 118. In some examples, the sensors 152 are knock sensors that count the number of grain knocks per unit of time or per unit of travel distance to provide an indication of the amount of grain loss occurring at the clean grain subsystem 118. The knock sensors on the right and left sides of the clean grain subsystem 118 can provide separate signals, or a combined or aggregate signal. In some examples, the sensors 152 can include a single sensor, as opposed to providing a separate sensor for each clean grain subsystem 118.

[0037] The separator loss sensors 148 provide output signals indicative of the amount of grain loss occurring on the left and right sides of the threshing machine 110. In some examples, the sensors 148 are knock sensors that count the number of grain knocks per unit of time or per unit of travel distance to provide an indication of the amount of grain loss occurring at the threshing machine 110. The knock sensors on the right and left sides of the threshing machine 110 can provide separate signals, or a combined or aggregate signal. In some examples, the sensors 148 can include a single sensor, as opposed to providing a separate sensor for each threshing machine 110.Figure 1 The separator loss sensors 148 can be associated with the left and right separators and can provide separate grain loss signals, or a combined or aggregate signal. In some cases, sensing grain loss in the separators can likewise be performed using a variety of different types of sensors.

[0038] The agricultural harvester 100 can also include other sensors and measurement mechanisms. For example, the agricultural harvester 100 can include one or more of the following sensors: a header height sensor that senses the height of the header 102 above the ground 111; a stabilization sensor that senses the oscillating or jounce motion (and amplitude) of the agricultural harvester 100; a residue setting sensor configured to sense whether the agricultural harvester 100 is configured to chop residue, create a windrow, etc.; a cleaner fan rotational speed sensor that senses the speed of the clean grain fan 120; a concave gap sensor that senses the gap between the threshing rotor 112 and the concave 114; a threshing rotor speed sensor that senses the rotor speed of the threshing rotor 112; a force sensor that senses the force required to drive the threshing rotor 112, such as a pressure sensor that senses the fluid (e.g., hydraulic fluid, air, etc.) pressure used to drive the threshing rotor 112, or a torque sensor that senses the torque used to drive the threshing rotor 112; a chaffer gap sensor that senses the size of the openings in the chaffer 122; a screen gap sensor that senses the size of the openings in the screen 124; a material other than grain (MOG) moisture sensor (such as a capacitive sensor) that senses the moisture level of the MOG passing through the agricultural harvester 100; one or more machine setting sensors configured to sense various configurable settings of the agricultural harvester 100; a machine orientation sensor that senses the orientation of the agricultural harvester 100; and a crop property sensor that senses various different types of crop properties, such as crop type, crop moisture, crop height, crop density, crop mass, crop volume, straw characteristics, grain characteristics, chaff characteristics, ear characteristics, crop color characteristics including color characteristics of components of the crop, such as ear color, chaff color, straw color, grain color, etc. The crop property sensor can also be configured to sense characteristics of crop constituents, such as the amount of constituents (e.g., oil, starch, protein, and other chemical classes) contained in the crop material or contained in components of the crop plant (e.g., grain). The crop property sensor is also configured to sense characteristics of the cut crop material as it is being processed by the agricultural harvester 100. For example, in some cases, the crop property sensor can sense: grain quality, such as broken grain, MOG levels; grain constituents, such as starch and protein; and grain feed rate as the grain travels through the feeder housing 106, the clean grain elevator 130, or other places in the agricultural harvester 100. The crop property sensor can also sense the feed rate of biomass through the feeder housing 106, through the separator 116, or other places in the agricultural harvester 100. The crop property sensor can also sense the 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 sensors can include one or more yield sensors that sense a yield of a crop being harvested by the agricultural harvester.

[0039] Prior to describing how the agricultural harvester 100 generates a functional prediction profile and uses the functional prediction profile for control, a brief description of some of the items on the agricultural harvester 100 and their corresponding operations will first be described.

[0040] To Figure 2 The description of FIG. 3 describes receiving a general type of prior information map and combining information from the prior information map with georeferenced sensor signals generated by in-field sensors, where the sensor signals are indicative of one or more agricultural properties, such as one or more non-machine properties or one or more machine properties of the agricultural harvester 100. Non-machine properties are any agricultural properties that are not related to a machine, such as the agricultural harvester 100. Non-machine properties can include many properties, such as properties of a field. Properties of a field can include, but are not limited to, surface features, such as topography, slope, surface quality, etc.; weed properties, such as weed intensity, weed type, etc.; soil property properties, such as soil type, soil moisture, soil cover, soil structure, etc.; crop property properties, such as crop population, crop height, crop volume, crop mass, crop moisture, crop density, crop state, stalk properties (such as stalk thickness or strength), chaff properties, color data (such as chaff color or straw color), pulverization properties, yield properties, dry down properties, pest reaction properties, drought reaction properties, weather reaction properties, etc.; or grain property properties, such as grain moisture, grain size, grain test weight, kernel properties (such as kernel size or weight, etc.). Other non-machine properties are within the scope of the present disclosure. Machine properties are any agricultural properties that are related to a machine, such as the agricultural harvester 100. Machine properties can include many properties, such as various machine settings or operating properties, such as ground speed, reel settings (such as reel height or speed), fan speed settings, table deck spacing or position, stalk rolling speed, header height, header orientation, machine forward direction, threshing rotor drive force, or engine load. Machine properties can also include other machine settings or operating properties. Machine properties can also include various properties of machine performance, such as levels of wear, quality of work, fuel consumption, and power utilization; or other machine properties. Other machine properties are within the scope of the present disclosure.

[0041] The relationship between the characteristic values obtained from the field sensor signals and the prior information map values is identified, 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 these 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, such as through 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, such as 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] Having described the overall method with reference to Figure 2 and FIG. 3, a more specific method for generating a functional prediction agricultural characteristic map that can be presented to an operator or user, used to control an agricultural harvester 100, or both is described with reference to FIG. 4 and Figure 5 Further, while the present discussion continues to be directed to agricultural harvesters (and particularly combine harvesters), 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 2It is shown that the agricultural harvester 100 illustratively includes one or more processors or servers 201, a data store 202, a geo-location sensor 204, a communication system 206, and one or more in-situ sensors 208 that sense one or more agricultural properties of the field contemporaneously with the harvesting operation. Agricultural properties can include any property that can have an influence on the harvesting operation. Some examples of agricultural properties include properties of the harvester, the field, the plants on the field, and the weather. Other types of agricultural properties are also included. The in-situ sensors 208 generate values corresponding to the sensed properties. 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 wide variety of other agricultural harvester functionality 220. For example, the in-situ sensors 208 include on-board sensors 222, remote sensors 224, and other sensors 226 that sense properties of the field during the course of the agricultural operation. The predictive model generator 210 illustratively includes a prior information variable-to-in-situ variable model generator 228, and the predictive model generator 210 can include other items 230. The control system 214 includes a communication system controller 229, an operator interface controller 231, a settings controller 232, a path planning controller 234, a feed rate controller 236, a header and reel controller 238, a conveyor belt controller 240, a table 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 wide variety of other subsystems 256.

[0044] Figure 2 It is also shown that the agricultural harvester 100 can receive a prior information map 258. As described below, the prior information map 258 includes, for example, a planting map or a map from a previous operation. However, the prior information map 258 can also encompass other types of data obtained prior to the harvesting operation, or a map from a previous operation. Figure 2It 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, levers, steering wheels, linkage mechanisms, pedals, buttons, dials, keyboards, user-actuatable elements on user interface display devices such as icons, buttons, etc., microphones and speakers (where voice recognition and speech synthesis are provided), and a wide variety of other types of control devices. Where a touch-sensitive display system is provided, the operator 260 can interact with the operator interface mechanism 218 using touch gestures. These examples described above 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 be used and are within the scope of the present disclosure.

[0045] The prior information map 258 can be downloaded using the communication system 206 or otherwise onto the agricultural harvester 100 and stored in the data storage 202. In some examples, the communication system 206 can be a cellular communication system, a system for communicating over a wide area network or a local area network, a system for communicating over a near field communication network, a communication system configured to communicate over any of a variety of other networks or a combination of networks. The communication system 206 can also include a system that facilitates downloading or transferring information to and from a secure digital (SD) card or a universal serial bus (USB) card or both a secure digital (SD) card and a universal serial bus (USB) card.

[0046] The geo-location sensor 204 illustratively senses or detects a geo-location or position of the agricultural harvester 100. The geo-location sensor 204 can include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from GNSS satellite transmitters. The geo-location sensor 204 can also include a real-time kinematic (RTK) component configured to improve the accuracy of position data derived from GNSS signals. The geo-location sensor 204 can include a dead reckoning system, a cellular triangulation system, or any of a variety of other geo-location sensors.

[0047] The field sensors 208 can be any of the field sensors described above with reference to Figure 1Any of the described sensors. The field sensors 208 include on-board sensors 222 that are mounted on-board the agricultural harvester 100. The field sensors 208 also include remote field sensors 224 that capture field information. The field sensors 208 can sense any of a number of characteristics. For example, the field sensors 208 can sense one or more characteristics of the operating environment of the agricultural harvester 100 (e.g., characteristics of the field), or one or more machine characteristics of the agricultural harvester 100 such as machine settings, operating characteristics, or machine performance data. Such sensors can include, but are not limited to, soil characteristic sensors; crop characteristic sensors; weed characteristic sensors; yield sensors; biomass sensors; tailings sensors; grain quality sensors; internal material distribution sensors; residue sensors; or machine characteristic sensors such as power characteristic sensors, speed sensors, machine orientation (e.g., pitch, roll, or yaw (direction)) sensors, machine performance sensors (e.g., fuel consumption sensors, grain loss sensors, etc.). In some examples, the field sensors can include, but are not limited to, perception sensors (e.g., forward-looking monocular or stereo camera systems and image processing systems) and image sensors (such as one or more clean grain cameras mounted to identify characteristics of vegetation traveling through the agricultural harvester 100) within the agricultural harvester 100. Field data includes data acquired from sensors on-board the agricultural harvester or acquired by any sensors that detect data during a harvesting operation. Some other examples of field sensors 208 are shown in Figure 4B

[0048] The predictive model generator 210 generates models that indicate relationships 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 maps seeding characteristic values to different locations in the field, and the field sensors 208 sense values indicative of biomass, the model of prior information variable to field variable model generator 228 generates a predictive biomass model that models the relationship between the seeding characteristic values and the biomass values. This is because various seeding characteristics can be indicative of resulting biomass of plants on the field of interest. For example, spacing (such as spacing between seeds in a common row or spacing between rows) can be indicative of vegetation density or vegetation population. Seeding characteristics and biomass are merely examples, and seeding characteristics can involve other characteristics sensed by one or more field sensors 208 for which the predictive model generator 210 can generate models.

[0049] ​The prediction model can also be generated based on the planting characteristic values from the prior information map 258 and the plurality of field data values generated by the field sensors 208. The prediction map generator 212 then uses the prediction model generated by the prediction model generator 210 to generate a functional prediction map 263 that predicts values of a characteristic (such as biomass or a biomass characteristic) sensed by the field sensors 208 at different locations in the field based on the prior information map 258.

[0050] In examples where the prior information map 258 is a planting map and the field sensors 208 sense values indicative of an agricultural characteristic, the prediction map generator 212 can use the planting characteristic values in the prior information map 258 and the model generated by the prediction model generator 210 to generate a functional prediction map 263 that predicts the agricultural characteristic at different locations in the field. As such, the prediction map generator outputs a prediction map 264.

[0051] In some examples, the type of values 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 values 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 values 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 values 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 and the type of data in the prior information map 258, and different than the other.

[0052] As Figure 2As shown in FIG. 2, the prediction map 264 predicts values of a sensed characteristic (sensed by the field sensors 208) or a characteristic related to the sensed characteristic at a plurality of locations across the field based on previous information values in the prior information map 258 at those locations and using a prediction model. For example, if the prediction model generator 210 has generated a prediction model that indicates a relationship between a planting characteristic value and a crop state, given the planting characteristic values at different locations across the field, the prediction map generator 212 generates the prediction map 264 that predicts values of the crop state at the different locations across the field. The planting characteristics obtained from the planting map at those locations and the relationship between the planting characteristic values and the crop state obtained from the prediction model are used to generate the prediction map 264. This is because various planting characteristics can be indicative of a resulting crop state of a crop on the field of interest. For example, a genotype of a planted seed, such as a seed hybrid, can affect the resulting crop state. For example, different crop hybrids are more or less susceptible to being in a down state, e.g., due to green snap, where a stalk breaks due to strong winds. The planting characteristics and the crop state are provided by way of example only. The planting characteristics can relate to various other characteristics sensed by the one or more field sensors 208 for which the prediction model generator 210 can generate a model. The prediction model generator 210 can generate a prediction model that indicates a relationship between a planting characteristic value and any of a number of characteristics sensed by the field sensors 208 or a number of characteristics related to the sensed characteristics, and the prediction map generator 212 can predict a prediction map 264 of values of the characteristics at different locations across the field. The planting characteristics obtained from the planting map at those locations and the relationship between the planting characteristic values and the characteristics sensed by the field sensors 208 can be used to generate the prediction map 264.

[0053] 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.

[0054] In some examples, the type of data in the prior information map 258 is different from the type of data sensed by the field sensors 208. While the type of data in the prediction map 264 is the same as the type of data sensed by the field sensors 208. For example, the prior information map 258 can be a planting map, and the variable sensed by the field sensors 208 can be yield. In such an example, the prediction map 264 can be a predicted yield map that maps predicted yield values to different geographic locations in the field. In another example, the prior information map 258 can be a planting map, and the variable sensed by the field sensors 208 can be crop height. In this example, the prediction map 264 can be a predicted crop height map that maps predicted crop height values to different geographic locations in the field.

[0055] Further, in some examples, the data type in the prior information map 258 is different from the data type sensed by the field sensors 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 field sensors 208. For example, the prior information map 258 can be a seeding map, and the variable sensed by the field sensors 208 can be crop height. In such an example, the prediction map 264 can be a predicted biomass map that maps predicted biomass to different geographic locations in the field.

[0056] In some examples, the prior information map 258 is generated from a previous pass through the field during a previous operation, and the data type is different from the data type sensed by the field sensors 208, while the data type in the prediction map 264 is the same as the data type sensed by the field sensors 208. For example, the prior information map 258 can be a seed population map generated during planting, and the variable sensed by the field sensors 208 can be stalk size. Then, the prediction map 264 can be a predicted stalk size map that maps predicted stalk size values to different geographic locations in the field. In another example, the prior information map 258 can be a seeding mix map, and the variable sensed by the field sensors 208 can be crop status, such as standing crop or fallen crop. Then, the prediction map 264 can be a predicted crop status map that maps predicted crop status values to different geographic locations in the field.

[0057] In some examples, the prior information map 258 is generated from a previous pass through the field during a previous operation, and the data type is the same as the data type sensed by the field sensors 208, and the data type in the prediction map 264 is also the same as the data type sensed by the field sensors 208. For example, the prior information map 258 can be a yield map generated during the previous year, and the variable sensed by the field sensors 208 can be yield. Then, the prediction map 264 can be a predicted yield map that maps predicted yield values to different geographic locations in the field. In such an example, the georeferenced prior information map 258 can be used by the prediction model generator 210 to generate a prediction model that models the relationship between the relative yield difference from the previous year on the prior information map 258 and the yield values sensed by the field sensors 208 during the current harvesting operation. The prediction model is then used by the prediction map generator 210 to generate the predicted yield map.

[0058] In another example, the prior information map 258 can be a weed intensity map generated during a previous operation, such as from a sprayer, and the variable sensed by the field sensor 208 can be weed intensity. Then, the prediction map 264 can be a predicted weed intensity map mapping predicted weed intensity values to different geographic locations in the field. In such an example, the weed intensity map at the time of spraying is georeferenced and provided as the prior information map 258 of weed intensity to the agricultural harvester 100. The field sensor 208 can detect the weed intensity at the geographic location in the field, and subsequently the prediction model generator 210 can construct a prediction model modeling the relationship between the weed intensity at the time of harvesting and the weed intensity at the time of spraying. This is because the sprayer will have affected the weed intensity at the time of spraying, but the weeds can again appear in similar areas at the time of harvesting. However, the weed areas at the time of harvesting are likely to have different intensities based on the time of harvesting, weather, weed type, and other factors.

[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 the field into one or more control zones based on the data values of the prediction map 264 associated with those adjacent portions. A control zone can include two or more contiguous portions of a field, such as a field, for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant. For example, the response time for changing a setting of a controllable subsystem 216 can not be sufficient to respond satisfactorily to changes in values contained in a map, such as the prediction map 264. In that 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, the control zones can be sized to reduce wear and tear due to excessive actuator movement from continuous adjustments. In some examples, there can be different sets of control zones for each controllable subsystem 216 or for a group of controllable subsystems 216. The 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 the control zones. Thus, a functional prediction map 263 as described herein can or can not include control zones. 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, such as the prediction map 264. In another example, the functional prediction map 263 includes control zones, such as the prediction control zone map 265. In some examples, if an intercrop production system is implemented, multiple crops can be present in the field at the same time. In that case, the prediction map generator 212 and the control zone generator 213 can identify the locations 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 the control zones can be added to the prediction control zone map 265 or a separate map showing only 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 for controlling 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, for example, based on the operator's observations to correct characteristics displayed on the map. The settings controller 232 can generate control signals based on the prediction map 264, the prediction control zone map 265, or both, to control various settings of the agricultural harvester 100. For example, the settings controller 232 can generate control signals for controlling the machine and header actuators 248. In response to the generated control signals, the machine and header actuators 248 operate to control, for example, one or more of the sieve and chaffer settings, concave gap, rotor settings, clean grain fan speed settings, header height, header functions, reel speed, reel position, conveyor functions where the agricultural harvester 100 is coupled to a conveyor header, corn header functions, in-cab distribution control, and other actuators 248 that affect other functions of the agricultural harvester 100. The path planning controller 234 illustratively generates control signals to control the steering subsystem 252 to steer the agricultural harvester 100 according to a desired path. The path planning controller 234 can control a path planning system to generate a route for the agricultural harvester 100 and can control the propulsion subsystem 250 and the steering subsystem 252 to steer the agricultural harvester 100 along the route. The feed rate controller 236 can control various subsystems, such as the propulsion subsystem 250 and the machine actuators 248, to control the feed rate or throughput based on the prediction map 264 or the prediction control zone map 265 or both. For example, when the agricultural harvester 100 approaches an upcoming crop area on a field where the biomass values are above a selected threshold, the feed rate controller 236 can reduce the speed of the machine 100 to maintain a constant feed rate of the biomass through the machine 100. The header and reel controller 238 can generate control signals to control the header or reel or other header functions. The conveyor belt controller 240 can generate control signals to control the conveyor belt or other 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 deck included on the header based on the prediction map 264 or the prediction control zone map 265 or both, and the residue system controller 244 can 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 for controlling the machine cleanout subsystem 254. For example, based on the different types of seeds or weeds passing through the machine 100, a particular type of machine cleanout operation or frequency of performing a cleanout process 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 FIG. 3 (collectively referred to herein as FIG. 3) illustrates a flowchart showing 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 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 planting map generated during a previous operation, or generated based on data from a previous operation on the field, such as a previous seed plant operation performed by a planter. Data for the prior information map 258 can also be collected in other ways. For example, the data can be collected based on aerial images or measurements taken the previous year or early in the current growing season or during another time. The information can also be based on data detected or collected in other ways, other than using aerial images. For example, data for the prior information map 258 can be communicated to the agricultural harvester 100 using the communication system 206 and stored in the data storage 202. Data for the prior information map 258 can also be provided to the agricultural harvester 100 in other ways using the communication system 206, and this is indicated by block 286 in the flowchart of FIG. 3. In some examples, the prior information map 258 can be received by the communication system 206.

[0065] At the start of a harvesting operation, the field sensors 208 generate sensor signals indicative of one or more field data values indicative of an agricultural characteristic, e.g., a non-machine characteristic such as a characteristic of the field or a machine characteristic such as a machine setting, operating characteristic, or characteristic of machine performance, as indicated by block 288. Examples of field sensors 208 are discussed with reference to blocks 222, 290, and 226. As explained above, the field sensors 208 include on-board sensors 222, remote field sensors 224 such as UAV-based sensors that collect field data on a single flight, shown in block 290, or other types of field sensors specified by field sensors 226. In some examples, data from on-board sensors is georeferenced using position, heading, or velocity data from the geolocation sensors 204.

[0066] The prediction model generator 210 controls the model generator 228 of prior information variables to field variables 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 characteristic of the type of data 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 characteristic or type of data or different characteristics or types of data.

[0067] The relationship or model generated by the prediction model generator 210 is provided to the prediction map generator 212. The prediction map generator 212 generates a prediction map 264 that uses the prediction model and the prior information map 258 to predict the values of the characteristic sensed by the field sensors 208 at different geographic locations in the field being harvested or values of different characteristics related to the characteristic sensed by the field sensors 208, as indicated by block 294.

[0068] It is noted that in some examples, the prior information map 258 can include two or more different maps, or two or more different layers of a single map. Each layer can represent a different data type than the data type of another layer, or the layers can have the same data type obtained at different times. Each of the two or more different maps, or each of the two or more different layers of a map, maps different types of variables to geographic locations in the field. In such examples, the prediction model generator 210 generates a prediction model that models relationships between the on-site data and each of the different variables mapped by the two or more different maps or the two or more different layers of a map. Similarly, the on-site sensors 208 can include two or more sensors, each of which senses a different type of variable. As such, the prediction model generator 210 generates a prediction model that models relationships between each type of variable mapped by the prior information map 258 and each type of variable sensed by the on-site sensors 208. The prediction map generator 212 can generate a functional prediction map 263 that uses the prediction model and each of the maps or layers of the prior information map 258 to predict values of each sensed characteristic (or characteristics related to the sensed characteristic) sensed by the on-site sensors 208 at different locations in the field being harvested.

[0069] The prediction map generator 212 configures the prediction map 264 such that the prediction map 264 is executable (or usable) 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 different controllable subsystems of the agricultural harvester 100, as indicated by block 296.

[0070] The control zone generator 213 can divide the prediction map 264 into control zones based on the values on the prediction map 264. Successive geolocated values that are within a threshold of each other can be grouped into a control zone. The threshold can be a default threshold, or the threshold 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 response of the control system 214, controllable subsystem 216, based on wear considerations, or based on other criteria, as indicated by block 295. The control zone generator 213 can configure a 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 location, the control zones on the prediction control zone map 265 related to geographic location, and the set values or control parameters used based on the predicted values on the prediction map 264 or zones on the prediction control zone map 265. In another example, the presentation includes more summary information or more detailed information. The presentation can also include a confidence level that indicates an accuracy of the predicted values on the prediction map 264 or the zones on the prediction control zone map 265 in agreement with measured values that can be measured by sensors on the agricultural harvester 100 as the agricultural harvester 100 moves through the field. Additionally, where the information is presented in more than one location, an authentication and authorization system can be provided to implement an authentication and authorization process. For example, there can be levels of individuals that are authorized to view and change the various maps and other presented information. By way of example, an onboard display device can show the various maps locally on the machine in near real-time, or the various maps can also be generated at one or more remote locations, or both. In some examples, each entity display device at each location can be associated with a personnel or user permission level. The user permission level can be used to determine which display identifiers are visible on the entity display device and which values the respective personnel 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 monitor, such as a monitor at a remote location, can be able to see the prediction map 264 on the display but be prevented from making any changes. A manager, possibly at a separate remote location, can be able to see all of the elements on the prediction map 264 and also be able to change the prediction map 264. In some cases, the prediction map 264 that is accessible and changeable by the remotely located manager can be used for machine control. This is one example of an authorization level 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.

[0071] 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 receipt by the control system 214 of inputs from the geo-location sensor 204 identifying the geo-location of the agricultural harvester 100. Block 302 represents the receipt by the control system 214 of sensor inputs indicative of the trajectory or heading of the agricultural harvester 100, and block 304 represents the receipt by the control system 214 of the speed of the agricultural harvester 100. Block 306 represents the receipt by the control system 214 of other information from the various field sensors 208.

[0072] At block 308, the control system 214 generates control signals to control the controllable subsystems 216 based on the prediction map 264 or the prediction control zone map 265 or both, and the inputs from the geo-location sensor 204 and any other field sensors 208. At block 310, the control system 214 applies the control signals to the controllable subsystems. It will be understood that the particular control signals generated and the particular controllable subsystems 216 being controlled can vary based on one or more different things. For example, the control signals generated and the controllable subsystems 216 being controlled can be based on the type of prediction map 264 or prediction control zone map 265 or both that is being used. Similarly, the control signals generated and the controllable subsystems 216 being controlled, as well as the timing of the control signals, can be based on various delays in the flow of the crop through the agricultural harvester 100 and the responsiveness of the controllable subsystems 216.

[0073] As an example, the generated prediction map 264 in the form of a predicted biomass map can be used to control one or more controllable subsystems 216. For example, the predicted biomass map can include speed values that are geographically referenced to locations within the field being harvested. The biomass values from the predicted speed map can be extracted and used to control the steering subsystem 252 and the propulsion subsystem 250. By controlling the steering subsystem 252 and the propulsion subsystem 250, the rate of material movement through the agricultural harvester 100 can be controlled. Similarly, the header height can be controlled to admit 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 yield with respect to location in the field, then control of the agricultural harvester 100 can be implemented. For example, if the values presented in the predicted yield map indicate that the yield ahead of the agricultural harvester 100 is higher on one portion of the header 102 than another portion of the header 102, then control of the header 102 can be implemented. For example, the conveyor speed on one side of the header 102 can be increased or decreased relative to the conveyor speed on the other side of the header 102 to account for the additional biomass. Thus, the geographically referenced values presented in the predicted yield map can be used to control the header and reel controller 238 to control the conveyor speed of the conveyor belts on the header 102. Additionally, the geographically referenced values obtained from the predicted biomass map or the predicted yield map, and geographically referenced values obtained from various other prediction maps, can be used to automatically change the header height by the header and reel controller 238 as the agricultural harvester 100 makes its way through the field. The foregoing examples relating to various controls using the predicted biomass map or the predicted yield map are provided by way of example only. Thus, a wide variety of other control signals can be generated using values obtained from the predicted biomass map, the predicted yield map, or other types of prediction maps to control one or more of the controllable subsystems 216.

[0074] At block 312, a determination is made as to whether the harvesting operation has been completed. If harvesting is not complete, the process proceeds to block 314, where the reading of the field sensor data from the geo-location sensor 204 and the field sensors 208 (and possibly other sensors) continues.

[0075] 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 prediction control zone map 265, the models generated by the prediction model generator 210, the zones generated by the control zone generator 213, the one or more control algorithms executed by the controllers in the control system 214, and other triggered learning.

[0076] The learning trigger criteria can include any of a variety of different criteria. Some examples of detecting trigger criteria are discussed with reference to blocks 318, 320, 321, 322, and 324. For example, in some examples, the triggered learning can involve recreating relationships used to generate the prediction model when a threshold amount of field sensor data is obtained from the field sensors 208. In such examples, the amount of field sensor data received from the field sensors 208 exceeding a threshold 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 threshold amount of field sensor data received from the field sensors 208 triggers the creation of new relationships represented by the prediction model generated by the prediction model generator 210. In addition, 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 for triggering the creation of a new prediction model.

[0077] In other examples, the learning trigger criteria can be based on how much the field sensor data from the field sensors 208 changes (such as changes over time or changes compared to a previous value). 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 a threshold below, 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, a prediction control zone map 265, or both. 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 with the new received field sensor data all or a portion of which is used by the prediction map generator 212 to generate the new prediction map 264. At block 320, the change in the field sensor data (such as the magnitude of the amount of data that exceeds the selected range, or the magnitude of the change in the relationship between the field sensor data and the information in the prior information map 258) can be used as a trigger for causing the generation of a new prediction model and prediction map. Consistent with the examples described above, the threshold, range, and defined amount can be set to a default value; set by an operator or user via user interface interaction; set by an automated system; or otherwise set.

[0078] 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 relearning by the prediction model generator 210, the prediction map generator 212, the control zone generator 213, the control system 214, or other item. 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.

[0079] In some cases, the operator 260 can also edit the prediction map 264 or the prediction control zone map 265 or both. The editing can change values on the prediction map 264, change the size, shape, location, or existence of control zones on the prediction control zone map 265, or both. Block 321 shows that the edited information can be used as a learning trigger criterion.

[0080] In some cases, the operator 260 can also observe that the automated 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 is being commanded by the control system 214. Thus, the manual change in settings by the operator 260 can cause the prediction model generator 210 to relearn the model, the prediction map generator 212 to regenerate the map 264, the control zone generator 213 to regenerate one or more control zones on the prediction control zone map 265, and the control system 214 to relearn the control algorithm or perform machine learning on one or more of the controller components 232-246 in the control system 214, as indicated in block 322. Block 324 represents using other triggered learning criteria.

[0081] In other examples, relearning can be performed periodically or intermittently, e.g., based on a selected time interval, such as a discrete time interval or a variable time interval, as indicated by block 326.

[0082] If relearning is triggered (whether based on a learning trigger criterion or based on a time interval elapsing), 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 based on the learning trigger criterion to generate a new prediction model, a new prediction map, new control zones, and a new control algorithm, respectively, as indicated by block 326. The new prediction model, the new prediction map, and the new control algorithm are generated using any additional data that has been collected since the last learning operation was performed. Performing relearning is indicated by block 328.

[0083] If the harvesting operation has been completed, the operation moves from block 312 to block 330, where one or more of the prediction map 264, the prediction control zone map 265, and the prediction model generated by the prediction model generator 210 are stored. The prediction map 264, the prediction control zone map 265, and the prediction model can be stored locally on the data storage 202, or transmitted to a remote system using the communication system 206 for later use.

[0084] It will be noted that while some examples herein describe the prediction model generator 210 and the prediction map generator 212 receiving a prior information map when generating a prediction model and a functional prediction map, respectively, in other examples, the prediction model generator 210 and the prediction map generator 212 can receive other types of maps when generating a prediction model and a functional prediction map, respectively, including a prediction map, such as a functional prediction map generated during a harvesting operation.

[0085] Figure 4A is Figure 1 A block diagram of a portion of the agricultural harvester 100 shown in FIG. 1. In particular, among other things, Figure 4A An example of the prediction model generator 210 and the prediction map generator 212 is shown in more detail. Figure 4A Information flow between the various components shown is also shown. The prediction model generator 210 receives a planting map 332 as a prior information map. The prediction model generator 210 also receives a geographic location 334, or an indication of a geographic location, from the geographic location sensor 204. The field sensors 208 illustratively include agricultural property sensors, such as an agricultural property sensor 336, as well as a processing system 338. In some cases, the agricultural property sensor 336 can be located on-board the agricultural harvester 100.

[0086] The agricultural properties detected by the processing system 338 can include any of a number of non-machine properties, such as properties of a field or properties of plants on the field (e.g., properties indicative of biomass or yield, crop condition properties such as downed crop data, crop size properties such as crop height, crop stalk diameter, or ear size), as well as various other non-machine properties of the operating environment of the agricultural harvester 100. The agricultural properties detected by the processing system 338 can also include any of a number of machine properties of the agricultural harvester 100 or another machine, such as machine settings, machine performance properties, or machine operating properties (e.g., a height of the header 102 from the field, a position or speed of the reel 164, a speed setting of the grain cleaning fan 120, a force required to drive the threshing rotor 112, or a forward speed of the agricultural harvester 100), as well as various other machine properties. Thus, the field sensors 208 can be any sensor that can detect an agricultural property, such as a non-machine property or a machine property.

[0087] The processing system 338 processes sensor data generated from the agricultural property sensors 336 to generate processed data, some examples of which are described below. For example, the agricultural property sensors 336 can be optical sensors, such as cameras or other devices that perform optical sensing. The optical sensors can generate images that are indicative of various agricultural properties, such as non-machine properties or machine properties of the agricultural harvester 100 or another machine, and related properties. The processing system 338 processes one or more sensor signals, such as images obtained from the optical sensors, to generate processed sensor data, such as processed image data, that identifies one or more non-machine properties, such as properties of a field, or processed sensor data that identifies one or more properties of the agricultural harvester 100, such as machine settings, operating properties, or properties of machine performance, or related properties.

[0088] The processing system 338 can also geolocate values received from the field sensors 208. For example, the location of the agricultural harvester when the signal from the field sensors 208 is received is not typically the exact location of the agricultural property on the field. This is because an amount of time elapses between when the agricultural harvester initially contacts the agricultural property and when the agricultural property is sensed by the field sensors 208. Thus, when georeferencing the sensed data, the temporal time between initially encountering the agricultural property and sensing the agricultural property by the field sensors 208 is taken into account. By doing so, the sensed property can be accurately georeferenced to a location in the field.

[0089] By way of example, an amount of time elapses between when the agricultural harvester initially encounters a plant and when a characteristic of the plant is sensed. For example, when detecting a yield characteristic based on sensing processed grain delivered to a storage location on the agricultural harvester, an amount of time can elapse between when the plant is encountered on the field and when the processed grain is sensed (such as in the storage location). Thus, when the sensed data is georeferenced, the temporal time between initially encountering the plant and sensing the grain from the plant by the field sensor 208 is taken into account. By doing so, the yield can be accurately georeferenced to a 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 100, the yield values are generally geolocated to the V-shaped region behind the agricultural harvester 100 as the agricultural harvester 100 travels in the forward direction. The processing system 338 apportions or allocates the total yield detected by the yield sensor during each time or measurement interval back to the earlier georeferenced zones based on the travel time of the crop from different portions of the agricultural harvester, such as different lateral positions along the width of the header of the agricultural harvester. For example, the processing system 338 apportions the measured total yield from a middle measurement interval or time back to the georeferenced zones traversed by the header of the agricultural harvester during different measurement intervals or times. The processing system 338 allocates or apportions the total yield from a particular measurement interval or time to the previously traversed georeferenced zones as part of the V-shaped region. Similarly, in the example where the field sensor 208 is a threshing rotor drive force sensor that generates a sensor signal indicative of biomass, the processing system 338 can geolocate the values, such as the biomass values, by calculating the time delay between when the crop is encountered on the field and when the crop will be threshed by the threshing rotor 112. In such an example, by taking into account the calculated time delay, the threshing rotor drive force characteristic can be correlated to a corrected location on the field as an indicator of biomass. This time delay can be based at least in part on the forward speed of the agricultural harvester 100. These are merely examples.

[0090] In some examples, for example, the property sensor 336 can rely on a wavelength of electromagnetic energy, and a way in which the electromagnetic energy is reflected, absorbed, attenuated, or transmitted through the biomass or harvested grain. The agricultural property sensor 336 can sense other electromagnetic properties of the biomass or harvested grain, such as a dielectric constant, as the material passes between two capacitive plates. The agricultural property sensor 336 can also rely on a physical interaction associated with the biomass or grain. For example, a signal can be generated by a piezoelectric sheet in response to an impact of the biomass or grain on the piezoelectric sheet, or a signal can be generated by a microphone or accelerometer in response to a sound or vibration generated due to an impact of the biomass or grain on another object. Other properties or interactions and sensors can also be used. In some examples, raw or processed data from the agricultural property sensor 336 can be presented to the operator 260 via the operator interface mechanism 218. The operator 260 can be on the agricultural harvester 100 or at a remote location.

[0091] Continuing the discussion with reference to examples in which the agricultural property sensor 336 is configured to sense an agricultural property, such as a non-machine property or a machine property of the agricultural harvester 100 or another machine, or a property related thereto, respectively. For purposes of the present disclosure, a non-machine property is any agricultural property that does not involve a machine. For example, non-machine properties can include properties of a field on which the agricultural harvester 100 is operating, among various other non-machine properties. It will be understood that non-machine properties can be sensed from outside of the agricultural harvester 100 or within the agricultural harvester 100. For purposes of the present disclosure, a machine property is any agricultural property related to a machine, such as the agricultural harvester 100 or another machine, and includes, for example, properties of machine settings, operating characteristics or machine performance, among other machine properties. It will be noted that in some examples, a machine property can also indicate a non-machine property, and vice versa. For example, a threshing rotor drive force (machine property) can indicate biomass (non-machine property).

[0092] It will be understood that these are merely examples, and other sensors mentioned above as examples of agricultural characteristic sensor 336 are also considered herein. Furthermore, it will be understood that the field sensor 208 (including agricultural characteristic sensor 336) can sense any of many agricultural characteristics. The prediction model generator 210, discussed below, can identify the relationship between one or more agricultural characteristics detected or represented in sensor data at a geographic location corresponding to the sensor data and one or more sowing characteristic values ​​from a sowing map (such as sowing map 332) corresponding to the same location in the field. Based on said relationship, the prediction model generator 210 generates a predictive agricultural characteristic model. Additionally, it will be understood that the prediction map generator 212, discussed below, can use the characteristic model generated by the prediction model generator 210 to generate a functional prediction map, such as a functional predictive agricultural characteristic map. The generated functional prediction map predicts one or more agricultural characteristics at different locations in the field based on the geographic reference sowing characteristic values ​​contained in sowing map 332 at the same location in the field.

[0093] like Figure 4A As shown, the exemplary prediction model generator 210 includes one or more of the following: a model generator 342 for non-machine traits to a population, a model generator 344 for non-machine traits to a genotype, a model generator 346 for machine traits to a population, and a model generator 347 for machine traits to a genotype. In other examples, the prediction model generator 210 may include models with... Figure 4A The examples shown are compared to those additional, fewer, or different components. Therefore, in some examples, the predictive model generator 210 may also include other objects 348. Other objects 348 may include other types of predictive model generators to generate other types of agricultural characteristic models. For example, other objects 348 may include other non-machine characteristic models or other machine characteristic models, such as models of non-machine characteristics versus other sowing characteristics or models of machine characteristics versus other sowing characteristics. Other sowing characteristics may include, for example: location (e.g., the geographical location of seeds in the field); spacing (e.g., the spacing between individual seeds and the spacing between seed rows); population, which can be derived from the spacing; orientation (e.g., the orientation of seeds in furrows and the orientation of seed rows); depth (e.g., seed depth and furrow depth); size (e.g., seed size); and genotype (e.g., seed type, seed hybrid, seed variety, seed cultivar, etc.). Other sowing characteristics may also include, for example, various characteristics of the seedbed or seed furrow.

[0094] The non-machine characteristic-to-population model generator 342 identifies a relationship between non-machine characteristics detected or represented in the sensor data 340 at geographic locations corresponding to the sensor data 340 and plant population values from the planting map 332 corresponding to the same locations in the field where the environmental characteristics were detected or corresponded to. Based on this relationship established by the environmental characteristic-to-population model generator 342, the environmental characteristic-to-population model generator 342 generates a predictive agricultural characteristic model. The predictive agricultural characteristic model is used by the non-machine characteristic map generator 352 to predict non-machine characteristics at different locations in the field based on georeferenced plant population values at the same locations in the field that are included in the planting map 332.

[0095] The non-machine characteristic-to-genotype model generator 344 identifies a relationship between non-machine characteristics detected or represented in the sensor data 340 at geographic locations corresponding to the sensor data 340 and genotype values from the planting map 332 corresponding to the same locations in the field where the non-machine characteristics were detected or corresponded to. Based on this relationship established by the non-machine characteristic-to-genotype model generator 344, the non-machine characteristic-to-genotype model generator 344 generates a predictive agricultural characteristic model. The predictive agricultural characteristic model is used by the non-machine characteristic map generator 352 to predict non-machine characteristics at different locations in the field based on georeferenced genotype values at the same locations in the field that are included in the planting map 332.

[0096] The machine characteristic-to-population model generator 346 identifies a relationship between machine characteristics detected or represented in the sensor data 340 at geographic locations corresponding to the sensor data 340 and plant population values from the planting map 332 corresponding to the same locations in the field where the machine characteristics were detected or corresponded to. Based on this relationship established by the machine characteristic-to-population model generator 346, the machine characteristic-to-population model generator 346 generates a predictive agricultural characteristic model. The predictive agricultural characteristic model is used by the machine characteristic map generator 354 to predict machine characteristics at different locations in the field based on georeferenced plant population values at the same locations in the field that are included in the planting map 332.

[0097] The machine characteristic to genotype model generator 347 identifies a relationship between machine characteristics detected or represented in the sensor data 340 at a geographic location corresponding to the sensor data 340 and genotype values from the planting map 332 corresponding to the same location in the field where the machine characteristics were detected or corresponded to, in the sensor data 340. Based on this relationship established by the machine characteristic to genotype model generator 347, the machine characteristic to genotype model generator 347 generates a predictive agricultural characteristic model. The predictive agricultural characteristic model is used by the machine characteristic map generator 354 to predict machine characteristics at different locations in the field based on the georeferenced genotype values at the same locations in the field included in the planting map 332.

[0098] According to the foregoing, the predictive model generator 210 is operable to generate a plurality of predictive agricultural characteristic models, such as one or more of the predictive agricultural characteristic models generated by the model generators 342, 344, 346, 347, or 348. In another example, two or more of the predictive agricultural characteristic models described above can be combined into a single predictive agricultural characteristic model that predicts two or more of the non-machine characteristics or machine characteristics based on planting characteristic values at different locations in the field. Any of these agricultural characteristic models, or combinations thereof, are collectively represented in Figure 4A as the characteristic models 350.

[0099] The predictive agricultural characteristic models 350 are provided to the prediction map generator 212. In Figure 4A examples, the prediction map generator 212 includes a non-machine characteristic map generator 352 and a machine characteristic map generator 354. In other examples, the prediction map generator 212 can include additional, fewer, or different map generators. As such, in some examples, the prediction map generator 212 can include other items 358, which can include other types of map generators for generating characteristic maps for other types of characteristics. The non-machine characteristic map generator 352 receives the planting map 332 and the predictive agricultural characteristic models 350 that predict non-machine characteristics based on planting characteristic values in the planting map 332, and generates a prediction map that predicts non-machine characteristics at different locations in the field.

[0100] The machine characteristic map generator 354 receives the planting map 332 and the predictive agricultural characteristic models 350 that predict machine characteristics based on planting characteristic values in the planting map 332, and generates a prediction map that predicts machine characteristics at different locations in the field.

[0101] The prediction map generator 212 outputs one or more functional predicted agricultural property maps 360 of one or more of the predicted non-machine properties or machine properties. Each of the functional predicted agricultural property maps 360 predicts a corresponding agricultural property at different locations in the field. Each of the generated functional predicted agricultural property maps 360 can be provided to the control zone generator 213, the control system 214, or both, as shown in Figure 2 The control zone generator 213 generates control zones and merges those control zones into the functional prediction maps (i.e., the prediction maps 360) to generate the functional prediction maps 360 with control zones. The functional prediction maps 360 (with or without control zones) can be provided to the control system 214, which generates control signals based on the functional prediction maps 360 (with or without control zones) to control one or more of the controllable subsystems 216.

[0102] Figure 4B is a block diagram showing some examples of real-time (live) sensors 208. In Figure 4B Some, or different combinations, of the sensors shown in Figure 4B Some of the possible live sensors 208 shown in Figure 4B As shown, the live sensors 208 can include operator input sensors 980, machine sensors 982, harvested material property sensors 984, field and soil property sensors 985, environmental property sensors 987, and they can include a wide variety of other sensors 226. The non-machine sensors 983 include the operator input sensors 980, the harvested material property sensors 984, the field and soil property sensors 985, the environmental property sensors 987, and can also include other sensors 226. The operator input sensors 980 can be sensors that sense operator input through the operator interface mechanisms 218. Thus, the operator input sensors 980 can sense user movements of a linkage mechanism, a joystick, a steering wheel, a button, a dial, or a pedal. The operator input sensors 980 can also sense user interaction with other operator input mechanisms, such as interaction with a touch-sensitive screen, a microphone that utilizes voice recognition, or any of a wide variety of other operator input mechanisms.

[0103] Machine sensors 982 can sense different characteristics of the agricultural harvester 100. For example, as discussed above, the machine sensors 982 can include the machine speed sensor 146, the separator loss sensor 148, the clean grain camera 150, the forward view image capture mechanism 151, the loss sensor 152, or the geographic position sensor 204, examples of which are described above. The machine sensors 982 can also include machine setting sensors 991 that sense machine settings. Examples of machine setting sensors 991 are described above with reference to FIG. 1. Figure 1Some examples of machine settings are described. A front equipment (e.g., header) position sensor 993 can sense a position of the header 102, reel 164, cutter 104, or other front equipment relative to a frame of the agricultural harvester 100. For example, the sensor 993 can sense a height of the header 102 above the ground. The machine sensors 982 can also include a front equipment (e.g., header) orientation sensor 995. The sensor 995 can sense an orientation of the header 102 relative to the agricultural harvester 100 or relative to the ground. The machine sensors 982 can include a stability sensor 997. The stability sensor 997 senses oscillatory or jounce motion (and amplitude) of the agricultural harvester 100. The machine sensors 982 can also include a residue setting sensor 999 configured to sense whether the agricultural harvester 100 is configured to chop residue, generate a windrow, or process residue in another manner. The machine sensors 982 can include a clean grain sieve fan speed sensor 951 that senses a speed of the clean grain fan 120. The machine sensors 982 can include a concave gap sensor 953 that senses a gap between the rotor 112 and the concave 114 on the agricultural harvester 100. The machine sensors 982 can include a chaffer gap sensor 955 that senses a size of openings in the chaffer 122. The machine sensors 982 can include a threshing rotor speed sensor 957 that senses a rotor speed of the rotor 112. The machine sensors 982 can include a rotor pressure sensor 959 that senses a pressure used to drive the rotor 112. The machine sensors 982 can include a screen gap sensor 961 that senses a size of openings in the screen 124. The machine sensors 982 can include a MOG moisture sensor 963 that senses a moisture level of the MOG passing through the agricultural harvester 100. The machine sensors 982 can include a machine orientation sensor 965 that senses an orientation of the agricultural harvester 100. The machine sensors 982 can include a material feed rate sensor 967 that senses a feed rate of material as the material travels through the feeder house bin 106, clean grain elevator 130, or other place in the agricultural harvester 100. The machine sensors 982 can include a biomass sensor 969 that senses a biomass traveling through the feeder house bin 106, the separator 116, or other place in the agricultural harvester 100. The machine sensors 982 can include a fuel consumption sensor 971 that senses a rate of fuel consumption of the agricultural harvester 100 over time.Machine sensors 982 can include a power utilization sensor 973 that senses power utilization in the agricultural harvester 100, such as which subsystems are utilizing power, or the rate at which these subsystems are utilizing power, or the distribution of power among these subsystems in the agricultural harvester 100. Machine sensors 982 can include a tire pressure sensor 977 that senses the inflation pressure in the tires 144 of the agricultural harvester 100. Machine sensors 982 can include a variety of other machine performance or machine characteristic sensors indicated by block 975. Machine performance or machine characteristic sensors 975 can sense machine performance or characteristics of the agricultural harvester 100.

[0104] Harvested material property sensors 984 can sense characteristics of the severed crop material as the agricultural harvester 100 is processing the crop material. Crop properties can include such things as crop type, crop moisture, grain quality such as broken grain, MOG levels, grain constituents such as starch and protein, MOG moisture, and other crop material properties. Other sensors can sense stalk "toughness", corn to cob adhesion, and other characteristics that can be advantageously used to control processing to better capture grain, reduce grain damage, reduce power consumption, reduce grain loss, etc.

[0105] Field and soil property sensors 985 can sense characteristics of the field and soil. Field and soil properties can include soil moisture, soil firmness, presence and location of standing water, soil type, and other soil and field characteristics.

[0106] Environmental characteristic sensors 987 can sense one or more environmental characteristics. The environmental characteristics can include such things as wind direction and speed, precipitation, fog, dust levels or other obstructions, or other environmental characteristics.

[0107] In some examples, Figure 4B One or more of the sensors illustrated in FIG. 3 are processed to receive processed data 340 and used as input to a model generator 210. The model generator 210 generates a model that indicates a relationship between the sensor data and one or more of the previous or predicted information maps. The model is provided to a map generator 212 that generates a map that maps predicted sensor data values or related characteristics corresponding to the sensors from the Figure 4B

[0108] Figure 5 ​is a flowchart of an example of the operation of the prediction model generator 210 and the prediction map generator 212 in generating the predicted agricultural property model 350 and the functional predicted agricultural property map 360, respectively. At block 362, the prediction model generator 210 and the prediction map generator 212 receive the previous planting map 332. At block 364, the processing system 338 receives one or more sensor signals from the field sensors 208, such as the agricultural property sensors 336. As discussed above, the agricultural property sensors 336 can be non-machine property sensors, as indicated by block 366; machine property sensors, as indicated by block 368; or another type of agricultural property sensor, as indicated by block 370.

[0109] At block 372, the processing system 338 processes the one or more received sensor signals to generate sensor data indicative of an agricultural property or related property present in the one or more sensor signals. At block 374, the sensor data can be indicative of one or more non-machine properties present or corresponding to a location on the field, such as a location in front of the combine harvester. In some cases, as indicated at block 376, the sensor data can be indicative of one or more machine properties present or corresponding to a location on the field. In some cases, as indicated at block 380, the sensor data can be indicative of another agricultural property.

[0110] At block 382, the prediction model generator 210 also obtains a geographic location corresponding to the sensor data. For example, the prediction model generator 210 can obtain a geographic location or an indication of a geographic location from the geographic location sensor 204 and determine an exact geographic location on the field to which the sensor data corresponds based on machine delays, machine speed, etc., such as the exact geographic location at which the sensor signals were generated or from which the sensor data 340 was derived.

[0111] At block 384, the prediction model generator 210 generates one or more prediction models, such as a predicted agricultural property model 350 modeling a relationship between planting property values obtained from the planting map 332 and properties or related properties sensed by the field sensors 208. For example, the prediction model generator 210 can generate a predicted agricultural property model modeling a relationship between planting property values and sensed agricultural properties, such as non-machine properties or machine properties or related properties obtained from the field sensors 208, indicated by the sensor data.

[0112] At block 386, the prediction model, such as the prediction agricultural characteristic model 350, is provided to the prediction map generator 212, which generates a functional prediction map, such as the functional prediction agricultural characteristic map 360, based on the planting map or the georeferenced planting characteristic values therein and the prediction agricultural characteristic model 350. In some examples, the functional prediction agricultural characteristic map 360 predicts non-machine characteristics, as indicated by block 388. In some examples, the functional prediction agricultural characteristic map 360 predicts machine characteristics, as indicated by block 390. In still other examples, the functional prediction agricultural characteristic map 360 predicts other objects, as indicated by block 392. For example, in other examples, the functional prediction agricultural characteristic map 360 can predict one or more machine characteristics and one or more non-machine characteristics, or vice versa. Additionally, the functional prediction agricultural characteristic map 360 can be generated during the course of the agricultural operation. As such, the functional prediction agricultural characteristic map 360 is generated as the agricultural operation is being performed as the agricultural harvester moves through the field performing the agricultural operation.

[0113] At block 394, the prediction map generator 212 outputs the functional prediction agricultural characteristic map 360. At block 391, the prediction map generator 212 outputs the functional prediction agricultural characteristic map 360 for presentation to the operator 260 and possible interaction by the operator 260. At block 393, the prediction map generator 212 can configure the functional prediction agricultural characteristic map for use by the control system 214. At block 395, the prediction map generator 212 can also provide the functional prediction agricultural characteristic map 360 to the control zone generator 213 to generate and incorporate control zones. At block 397, the prediction map generator 212 can also otherwise configure the functional prediction agricultural characteristic map 360. The functional prediction agricultural characteristic map 360, with or without control zones, is provided to the control system 214. At block 396, the control system 214 generates control signals based on the prediction characteristic map 360, with or without control zones, to control the controllable subsystems 216.

[0114] The control system 214 can generate control signals to control the cutting table or other machine actuators 248. The control system 214 can generate control signals to control the propulsion subsystem 250. The control system 214 can generate control signals to control the steering subsystem 252. The control system 214 can generate control signals to control the residue subsystem 138. The control system 214 can generate control signals to control the machine clean grain subsystem 254. The control system 214 can generate control signals to control the threshing machine 110. The control system 214 can generate control signals to control the material handling subsystem 125. The control system 214 can generate control signals to control the crop clean grain subsystem 118. The control system 214 can generate control signals to control the communication system 206. The control system 214 can generate control signals to control the operator interface mechanism 218. The control system 214 can generate control signals to control various other controllable subsystems 256.

[0115] Thus, it can be seen that the present system obtains a priori information maps that map characteristics from previous operations, such as seeding characteristic values, or information to different locations in a field. The present system also uses sensed field sensor data from one or more field sensors, and generates a model that models a relationship between agricultural characteristics or related characteristics sensed using the field sensors, such as non-machine characteristics, machine characteristics, or another agricultural characteristic that can be sensed by a field sensor or indicated by a characteristic sensed by a field sensor, and characteristics mapped in the a priori information maps. Thus, the present system uses the model, the field data, and the a priori information maps to generate a functional prediction map, and the generated functional prediction map can be configured for use by a control system to present to a local or remote operator or other user or both. For example, the control system can use the map to control one or more systems of an agricultural harvester.

[0116] The present discussion has mentioned processors and servers. In some examples, processors and servers include computer processors with associated memory and timing circuitry, not separately shown. Processors and servers are functional parts of systems or devices to which they belong, are activated by, and facilitate the functioning of other components or articles of the systems.

[0117] Moreover, 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-activatable operator interface mechanisms disposed thereon. For example, the user-activatable operator interface mechanisms can include text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. The user-activatable operator interface mechanisms can also be actuated in a variety of different ways. For example, the user-activatable operator interface mechanisms can be actuated using operator interface mechanisms such as a pointing device (such as a trackball or mouse, a hardware button, a switch, a joystick or keyboard, a thumb switch or thumb pad, etc.), a virtual keyboard or other virtual actuator. Additionally, where the screen displaying the user-activatable operator interface mechanisms is a touch-sensitive screen, the user-activatable operator interface mechanisms can be activated using touch gestures. Furthermore, the user-activatable operator interface mechanisms can be activated using voice commands that utilize voice recognition functionality. Voice recognition can be implemented using voice detection devices such as microphones and software for recognizing the detected voice and executing commands based on the received voice.

[0118] A number of data stores have also been discussed. It will be noted that each data store can be divided into a plurality of data stores. In some examples, one or more of the data stores can be local to the system accessing the data store, all of the data stores can be located remotely from the system utilizing the data stores, or one or more data stores can be local while others are remote. All of these configurations are contemplated by the present disclosure.

[0119] Likewise, the figures show a number of blocks and assign functions to each block. It will be noted that fewer blocks can be used to illustrate the functionality, that functionality can be assigned to more blocks, and that some functionality can be removed from the illustrated blocks. In different examples, some functionality can be added and some can be removed.

[0120] It is noted that the above discussion has described various different systems, components, logic, and interactions. It will be understood that any or all of such systems, components, logic, or interactions can be implemented by hardware items that perform the functions associated with those systems, components, logic, or interactions, such as a processor, a memory, or other processing component, 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 memory and subsequently executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic, and interactions are 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.

[0121] Figure 6 is a block diagram of an agricultural harvester 600, which can be similar to the agricultural harvester 100 shown in Figure 2 The agricultural harvester 600 communicates with elements in the remote server architecture 500. In some examples, the remote server architecture 500 provides computing, software, data access, and storage services that do not require end users to know the physical location or configuration of the system that is delivering the services. In various examples, the remote server can deliver services over a wide area network, such as the Internet, using appropriate protocols. For example, the remote server can deliver an application over a wide area network and can be accessed through a web browser or any other computing component. Figure 2 The software or components shown in and the data associated therewith can all be stored on servers at a remote location. Computing resources in the remote server environment can be consolidated at a remote data center location, or computing resources can be distributed to multiple remote data centers. The remote server infrastructure can deliver services through a shared data center, even though the service appears as a single point of access for the user. Thus, the components and functionality described herein can be provided from a remote server located 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 onto a client device.

[0122] In the example shown in Figure 6 some items are similar to those shown in Figure 2 and those items are similarly numbered. Figure 6 It is specifically shown that the prediction model generator 210 or the prediction map generator 212 or both can be located at a server location that is remote from the agricultural harvester 600. Thus, in Figure 6In the example shown in FIG. 6, the agricultural harvester 600 accesses the system through the remote server location 502.

[0123] 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 the system 200 can be located at the remote server location 502, while other elements can be located elsewhere. By way of example, the data store 202 can be located at a location separate from the location 502 and accessed via a remote server at the location 502. Regardless of where the elements are located, the elements can be accessed by the agricultural harvester 600 directly over a network, such as a wide area network or a local area network; the elements can be hosted by a server at a remote site; or the elements can be provided as a server or accessed by a connecting server located 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, physical carriers can be used instead of or in addition to electromagnetic wave carriers. In some examples, where there is an overlap or absence of wireless telecommunication service coverage, another machine, such as a fueling truck or other mobile machine or vehicle, can have an automated, semi-automated, or manual information collection system. When the combine harvester 600 approaches the machine containing the information collection system, such as a fueling truck prior to fueling, the information collection system uses any type of ad-hoc wireless connection to collect information from the combine harvester 600. The collected information can then be forwarded to another network when the machine containing the received information reaches a location where wireless telecommunication coverage or other wireless coverage is available. For example, a fueling truck can enter an area with wireless communication coverage as it travels to a location to fuel other machines or as it is at a primary fuel storage location. All of these architectures are contemplated herein. Additionally, information can be stored onto the combine harvester 600 until the combine harvester 600 enters an area with wireless communication coverage. The combine harvester 600 itself can transmit the information to another network.

[0124] It will also be noted that, Figure 2 Elements of the system 200 or portions thereof can be located 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 cell phone, a smart phone, a multimedia player, a personal digital assistant, and the like.

[0125] In some examples, the remote server architecture 500 can include network security measures. Without limitation, these measures can include data encryption on storage devices, data encryption sent between network nodes, authentication of personnel or processes accessing data, and the use of ledgers to record metadata, data, data transmissions, data access, and data transformations. In some examples, the ledgers can be distributed and immutable (e.g., implemented as blockchains).

[0126] Figure 7 is a simplified block diagram of one illustrative embodiment of a handheld or mobile computing device that can be used as a user's or client's handheld device 16 in which the present system (or a portion thereof) can be deployed. For example, a mobile device can be deployed in the cab of an agricultural harvester 100 for generating, processing, or displaying the graphs discussed above. Figures 8-9 is an example of a handheld or mobile device.

[0127] Figure 7 A general block diagram of components of a client device 16 is provided, which can run some of the components shown in Figure 2 interact with some of the components shown in Figure 2 In the device 16, a communications link 13 is provided that allows the handheld device to communicate with other computing devices and, in the case of certain examples, automatically provide a channel or conduit for receiving information such as by scanning. Examples of the communications link 13 include allowing communication over one or more communication protocols, such as protocols for providing cellular wireless service that provides access to a network, and protocols that provide local wireless connections to a network.

[0128] 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 communicate with a processor 17 (which can also be embodied as a processor or server according to 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 positioning system 27.

[0129] In one example, the I / O components 23 are provided to facilitate input and output operations. The I / O components 23 for various examples of the 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 displays, speakers, and / or printer ports. Other I / O components 23 can also be used.

[0130] The clock 25 illustratively includes a real-time clock component that outputs time and date. The clock 25 can also illustratively provide timing functions to the processor 17.

[0131] The positioning system 27 illustratively includes a component that outputs the current geographic position of the device 16. For example, this can include a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. The positioning system 27 can also include mapping software or navigation software that generates desired maps, navigation routes, and other geographic functions, for example.

[0132] The memory 21 stores an operating system 29, network settings 31, applications 33, application configuration settings 35, data stores 37, communication drivers 39, and communication configuration settings 41. The memory 21 can include all types of tangible volatile and non-volatile computer-readable memory devices. It can also include, and can be included within, computer storage media (described below). The memory 21 stores computer-readable instructions that, when executed by the processor 17, cause the processor to perform computer-implemented steps or functions according to the instructions. The processor 17 can also be activated by other components to facilitate their functions.

[0133] Figure 8 One example is shown in which the device 16 is a tablet computer 600. In Figure 8 the computer 600 is shown with a user interface display screen 602. The screen 602 can be a touch screen or a pen-enabled interface that receives input from a pen or stylus. The tablet computer 600 can also use an on-screen virtual keyboard. Of course, the computer 600 can also be attached to a keyboard or other user input device through a suitable attachment mechanism, such as a wireless link or USB port, for example. The computer 600 can also illustratively receive voice input.

[0134] Figure 9 Similarly Figure 8 , except that the device is a smart phone 71. The smart phone 71 has a touch-sensitive display 73 that displays icons or tiles or other user input mechanisms 75. The user can use the mechanisms 75 to run applications, make phone calls, perform data transfer operations, etc. Typically, the smart phone 71 is built on a mobile operating system and provides more advanced computing capability and connectivity than a feature phone.

[0135] Note that other forms of the device 16 are possible.

[0136] Figure 10 is one example of a computing environment in which elements of Figure 2 may be deployed. See Figure 10An example system for implementing some embodiments includes a computing device, in the form of a computer 810, programmed to operate as discussed above. The components of computer 810 can include, but are not limited to, a processing unit 820 (which can include a processor or server according to 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 described memory and programs can be deployed in respective portions of Figure 10

[0137] 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 include computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. 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.

[0138] ​System memory 830 includes computer storage media in the form of volatile and / or non-volatile memory, or both, such as read-only memory (ROM) 831 and random access memory (RAM) 832. A basic input / output system 833 (BIOS) (containing basic routines) is typically stored in ROM 831, which facilitates (e.g., during startup) the transfer of information between components within computer 810. RAM 832 typically contains data and / or program modules, or both, that are readily accessible and / or currently being processed on or operated by unit 820. This is by way of example, not limitation. Figure 10 The operating system 834, application program 835, other program modules 836, and program data 837 are shown.

[0139] Computer 810 may also include other removable / non-removable volatile / non-volatile computer storage media. This is by way of example only. Figure 10 A hard disk drive 841, an optical disk drive 855, and a non-volatile optical disk 856 are shown for reading from or writing to non-removable non-volatile media. The hard disk drive 841 is typically connected to the system bus 821 via a non-removable disk storage interface (such as interface 840), and the optical disk drive 855 is typically connected to the system bus 821 via a removable storage interface (such as interface 850).

[0140] Alternatively, or additionally, the functions described herein may be performed at least in part by one or more hardware logic components. For example, but not limited to, illustrative types of hardware logic components that may be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (e.g., ASICs), application-specific standard products (e.g., ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and the like.

[0141] The above discussion and Figure 10 The driver and its associated computer storage media shown provide storage for computer-readable instructions, data structures, program modules, and other data for the computer 810. Figure 10 For example, hard disk drive 841 is shown storing operating system 844, application program 845, other program modules 846, and program data 847. It should be noted that these components may be the same as or different from operating system 834, application program 835, other program modules 836, and program data 837.

[0142] 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 or cursor control device 861, such as a mouse, trackball or touch pad. Other input devices (not shown) can include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 820 through a user input interface 860 that is coupled to the system bus. Visual data is displayed on a visual display 891 or other type of display device coupled to the system bus 821 via an interface, such as a video interface 890. In addition to the monitor, computers can also include other peripheral output devices such as speakers 897 and a printer 896, which can be connected through an output peripheral interface 895.

[0143] The computer 810 is operated 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 881 has been illustrated. The logical connections depicted include a local area network (LAN) 871 and a wide area network (WAN) 873, but can also include other networks. Such networking environments are commonplace in

[0144] 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. By way of example, Figure 10 It is shown that a remote application 885 can reside on the remote computer 880.

[0145] It should also be noted that the 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 others. All these possibilities are to be considered as within the scope of the present disclosure.

[0146] Example 1 is an agricultural work machine comprising:

[0147] a communication system that receives a prior information map, the prior information map comprising values of a seeding property corresponding to different geographical locations in a field;

[0148] a geographical location sensor that detects a geographical location of the agricultural work machine;

[0149] a field sensor that detects a value of an agricultural property corresponding to the geographical location;

[0150] a prediction model generator that generates a predictive agricultural model based on the value of the seeding characteristic in the prior information map at the geographic location and the value of the agricultural characteristic corresponding to the geographic location detected by the on-site sensor, the predictive agricultural model modeling a relationship between the seeding characteristic and the agricultural characteristic; and

[0151] a prediction map generator that generates a functional predictive agricultural characteristic map of the field based on the value of the seeding characteristic in the prior information map and based on the predictive agricultural model, the functional predictive agricultural characteristic map mapping predicted values of the agricultural characteristic to the different geographic locations in the field.

[0152] Example 2 is the agricultural work machine of any or all preceding examples, wherein the prediction map generator configures the functional predictive agricultural characteristic map for use by a control system, the control system generating control signals to control controllable subsystems on the agricultural work machine based on the functional predictive agricultural characteristic map.

[0153] Example 3 is the agricultural work machine of any or all preceding examples, wherein the on-site sensor comprises:

[0154] an optical sensor configured to detect an image indicative of the agricultural characteristic.

[0155] Example 4 is the agricultural work machine of any or all preceding examples, wherein the optical sensor is oriented to detect an image of at least a portion of the field and further comprises:

[0156] an image processing system configured to process the image to identify in the image a value of the agricultural characteristic indicative of the agricultural characteristic.

[0157] Example 5 is the agricultural work machine of any or all preceding examples, wherein the on-site sensor on the agricultural work machine is configured to detect a value of a non-machine characteristic corresponding to the geographic location as the value of the agricultural characteristic.

[0158] Example 6 is the agricultural work machine of any or all previous examples, wherein the prior information map includes genotype values corresponding to different geographic locations in the field as values for the seeding characteristic, and wherein the predictive model generator is configured to identify a relationship between the genotype values and the non-machine characteristic based on the values for the non-machine characteristic corresponding to the geographic locations and the genotype values at the geographic locations in the prior information map, the predictive characteristic model being configured to receive genotype values as model input and generate predicted values for the non-machine characteristic as model output based on the identified relationship.

[0159] Example 7 is the agricultural work machine of any or all previous examples, wherein the prior information map includes population values corresponding to different geographic locations in the field as values for the seeding characteristic, and wherein the predictive model generator is configured to identify a relationship between the population values and the non-machine characteristic based on the values for the non-machine characteristic corresponding to the geographic locations and the population values at the geographic locations in the prior information map, the predictive characteristic model being configured to receive population values as model input and generate predicted values for the non-machine characteristic as model output based on the identified relationship.

[0160] Example 8 is the agricultural work machine of any or all previous examples, wherein the on-board sensor of the agricultural work machine is configured to detect values for a machine characteristic corresponding to the geographic locations as values for the agricultural characteristic.

[0161] Example 9 is the agricultural work machine of any or all previous examples, wherein the prior information map includes genotype values corresponding to different geographic locations in the field as values for the seeding characteristic, and wherein the predictive model generator is configured to identify a relationship between the genotype values and the machine characteristic based on the values for the machine characteristic corresponding to the geographic locations and the genotype values at the geographic locations in the prior information map, the predictive characteristic model being configured to receive genotype values as model input and generate predicted values for the machine characteristic as model output based on the identified relationship.

[0162] Example 10 is the agricultural work machine of any or all preceding examples, wherein the prior information map includes population values corresponding to different geographic locations in the field as values of the seeding characteristic, and wherein the prediction model generator is configured to identify a relationship between the population values and the machine characteristic based on values of the machine characteristic corresponding to the geographic locations and the population values at the geographic locations in the prior information map, the prediction characteristic model being configured to receive a population value as a model input and generate a predicted value of the machine characteristic as a model output based on the identified relationship.

[0163] Example 11 is a computer-implemented method of generating a functional predictive agriculture map, the method comprising:

[0164] receiving a prior information map at an agricultural work machine, the prior information map including values of a seeding characteristic corresponding to different geographic locations in a field;

[0165] detecting a geographic location of the agricultural work machine;

[0166] detecting, with an on-site sensor, a value of an agricultural characteristic corresponding to the geographic location;

[0167] generating a predictive agriculture model that models a relationship between the agricultural characteristic and the seeding agricultural characteristic; and

[0168] controlling a prediction map generator to generate a functional predictive agriculture map of the field based on the values of the seeding characteristic in the prior information map and based on the predictive agriculture model, the functional predictive agriculture map mapping predicted values of the agricultural characteristic to different geographic locations in the field.

[0169] Example 12 is the computer-implemented method of any or all preceding examples, and further comprising:

[0170] configuring the functional predictive agriculture map for use in a control system that generates control signals to control controllable subsystems on the agricultural work machine based on the functional predictive agriculture map.

[0171] Example 13 is the computer-implemented method of any or all preceding examples, wherein receiving a prior information map comprises:

[0172] receiving a seeding map including genotype values corresponding to different geographic locations in the field as values of the seeding characteristic.

[0173] Example 14 is the computer-implemented method of any or all preceding examples, wherein generating a predictive agriculture model comprises:

[0174] identifying a relationship between the population value and the agricultural characteristic based on a value of the agricultural characteristic corresponding to the geographic location detected by the field sensor and the population value at the geographic location in the planting map; and

[0175] controlling a predictive model generator to generate the predictive agricultural model that receives a population value as a model input and generates a predicted value of the agricultural characteristic as a model output based on the identified relationship.

[0176] Example 15 is a computer-implemented method according to any or all preceding examples, wherein receiving a priori information map comprises:

[0177] receiving a planting map that includes population values corresponding to different geographic locations in the field as values of the planting characteristic.

[0178] Example 16 is a computer-implemented method according to any or all preceding examples, wherein generating a predictive agricultural model comprises:

[0179] identifying a relationship between the population value and the agricultural characteristic based on a value of the agricultural characteristic corresponding to the geographic location detected by the field sensor and the population value at the geographic location in the planting map; and

[0180] controlling a predictive model generator to generate the predictive agricultural model that receives a population value as a model input and generates a predicted value of the agricultural characteristic as a model output based on the identified relationship.

[0181] Example 17 is a computer-implemented method according to any or all preceding examples, further comprising:

[0182] controlling an operator interface mechanism to present the functional predicted agricultural characteristic map.

[0183] Example 18 is an agricultural work machine comprising:

[0184] a communication system that receives a planting map indicating values of a planting characteristic corresponding to different geographic locations in a field;

[0185] a geographic location sensor that detects a geographic location of the agricultural work machine;

[0186] a field sensor that detects a value of an agricultural characteristic corresponding to the geographic location;

[0187] a prediction model generator that generates a prediction model based on a value of the sowing characteristic at the geographic location in the sowing map and a value of the agricultural characteristic corresponding to the geographic location detected by the field sensor, the prediction model identifying a relationship between the sowing characteristic and the agricultural characteristic; and

[0188] a prediction map generator that generates a functional prediction map of the field based on the values of the sowing characteristic in the sowing map and based on the prediction model, the functional prediction map mapping predicted values of the agricultural characteristic to different geographic locations in the field.

[0189] Example 19 is the agricultural work machine of any or all preceding examples, wherein the sowing map includes a genotype value corresponding to different geographic locations in the field as the value of the sowing characteristic, and wherein the prediction model generator is configured to identify a relationship between the genotype value and the agricultural characteristic based on a value of the agricultural characteristic corresponding to the geographic location detected by the field sensor and the genotype value at the geographic location in the sowing map, the prediction model being configured to receive a genotype value as a model input and generate a predicted value of the agricultural characteristic as a model output based on the identified relationship.

[0190] While the subject matter has been described in language specific to structural features and / 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 comprising: a communication system (206) that receives a prior information map (258) that includes values of a seeding characteristic corresponding to different geographic locations in a field, the seeding characteristic including a genotype or a population; a geographic location sensor (204) that detects a geographic location of an agricultural work machine (100); a field sensor (208) that detects values of an agricultural characteristic corresponding to the geographic location; a predictive model generator (210) that generates a predictive agricultural model based on the value of the seeding characteristic at the geographic location in the prior information map (258) and the value of the agricultural characteristic corresponding to the geographic location detected by the field sensor (208), the predictive agricultural model modeling a relationship between the seeding characteristic and the agricultural characteristic; and a predictive map generator (212) that generates a functional predictive agricultural characteristic map of the field based on the values of the seeding characteristic in the prior information map (258) and based on the predictive agricultural model, the functional predictive agricultural characteristic map mapping predicted values of the agricultural characteristic to the different geographic locations in the field.

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

3. The agricultural system of claim 1, wherein, The field sensor includes: an optical sensor configured to detect an image indicative of the agricultural characteristic.

4. The agricultural system of claim 3, wherein, The optical sensor is oriented to detect an image of at least a portion of the field and further includes: an image processing system configured to process the image to identify in the image values of the agricultural characteristic indicative of the agricultural characteristic.

5. The agricultural system of claim 1, wherein, The field sensor is configured to detect values of a non-machine characteristic corresponding to the geographic location as values of the agricultural characteristic.

6. The agricultural system of claim 5, wherein, The predictive model generator is configured to identify a relationship between the genotype and the non-machine characteristic based on the value of the non-machine characteristic corresponding to the geographic location and the value of the genotype at the geographic location in the prior information map, a predictive characteristic model configured to receive a genotype value as a model input and generate a predicted value of the non-machine characteristic as a model output based on the identified relationship.

7. The agricultural system of claim 5, wherein, The predictive model generator is configured to identify a relationship between the population and the non-machine characteristic based on the value of the non-machine characteristic corresponding to the geographic location and a population value at the geographic location in the prior information map, a predictive characteristic model configured to receive a population value as a model input and generate a predicted value of the non-machine characteristic as a model output based on the identified relationship.

8. The agricultural system of claim 1, wherein, The field sensor is configured to detect values of a machine characteristic corresponding to the geographic location as values of the agricultural characteristic.

9. A computer-implemented method of generating a functional predictive agronomic map, the method comprising: receiving a prior information map (258) comprising values of a seeding characteristic corresponding to different geographic locations in a field, the seeding characteristic comprising a genotype or population; detecting a geographic location of an agricultural work machine (100); detecting, with an in-field sensor (208), a value of an agricultural characteristic corresponding to the geographic location; generating a predictive agricultural model modeling a relationship between the agricultural characteristic and the seeding characteristic; and controlling a predictive map generator (212) to generate a functional predictive agronomic map of the field based on the values of the seeding characteristic in the prior information map (258) and based on the predictive agricultural model, the functional predictive agronomic map mapping predicted values of the agricultural characteristic to different geographic locations in the field.

10. An agricultural system comprising: a communication system (206) receiving a seeding map indicating values of a seeding characteristic corresponding to different geographic locations in a field, the seeding characteristic comprising a genotype or population; a geographic location sensor (204) detecting a geographic location of an agricultural work machine (100); an in-field sensor (208) detecting a value of an agricultural characteristic corresponding to the geographic location; a predictive model generator (210) generating a predictive model based on a seeding characteristic value at the geographic location in the seeding map and the value of the agricultural characteristic corresponding to the geographic location detected by the in-field sensor (208), the predictive model identifying a relationship between the seeding characteristic and the agricultural characteristic; and a predictive map generator (212) generating a functional predictive map of the field based on the values of the seeding characteristic in the seeding map and based on the predictive model, the functional predictive map mapping predicted values of the agricultural characteristic to different geographic locations in the field.

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