CN113298670A

CN113298670A - Predictive machine map generation and control system

Info

Publication number: CN113298670A
Application number: CN202110157827.3A
Authority: CN
Inventors: 内森·R·范迪克; 巴努·基兰·雷迪·帕拉; 诺埃尔·W·安德森
Original assignee: Deere and Co
Current assignee: Deere and Co
Priority date: 2020-02-06
Filing date: 2021-02-04
Publication date: 2021-08-24

Abstract

One or more informational maps are obtained by the agricultural work machine. The one or more information maps map one or more agricultural trait values at different geographic locations of the field. As the agricultural work machine moves through the field, the field sensors on the agricultural work machine sense agricultural characteristics. The prediction map generator generates a prediction map that predicts predicted agricultural characteristics at different locations in the field based on relationships between values in the one or more information maps and agricultural characteristics sensed by the field sensors. The prediction map may be output and used for automatic machine control.

Description

Predictive machine map generation and control system

Cross Reference to Related Applications

This application is a continuation-in-part application and claims priority to U.S. patent application serial No. 16/783475 filed on 6.2.2020, U.S. patent application serial No. 16/783511 filed on 6.2.2.2020, U.S. patent application serial No. 16/380531 filed on 10.4.2019, and U.S. patent application serial No. 16/171978 filed on 26.10.2018, the contents of which are hereby incorporated by reference in their entirety.

Technical Field

The present description relates to agricultural machines, forestry machines, construction machines and lawn management machines.

Background

There are various different types of agricultural machines. Some agricultural machines include harvesters, such as combine harvesters, sugar cane harvesters, cotton harvesters, self-propelled feed harvesters, and windrowers. Some harvesters may also be equipped with different types of headers to harvest different types of crops.

The topographical characteristics can have several detrimental effects on harvesting operations. For example, pitch or roll of the harvester may hinder the performance of the harvester as it passes the tilt feature. Thus, the operator may attempt to modify the control of the harvester when encountering a slope during a harvesting operation.

The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.

Disclosure of Invention

One or more informational maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural trait values at different geographic locations of the field. As the agricultural work machine moves through the field, the field sensors on the agricultural work machine sense agricultural characteristics. The prediction map generator generates a prediction map that predicts a predicted agricultural characteristic for 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 field sensor. The prediction map may be output and used for automatic machine control.

This summary is provided to introduce a selection of concepts in a simplified form 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 as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to examples that solve any or all disadvantages noted in the background.

Drawings

Fig. 1 is a partially pictorial partially schematic illustration of an example of a combine harvester.

Fig. 2 is a block diagram illustrating some portions of an agricultural harvester in more detail according to some examples of the present disclosure.

Fig. 3A-3B (collectively referred to herein as fig. 3) illustrate a flow chart illustrating an example of the operation of an agricultural harvester when generating a map.

FIG. 4A is a block diagram illustrating one example of a prediction model generator and a prediction graph generator.

Fig. 4B is a block diagram illustrating a field sensor.

Fig. 5 is a flow chart illustrating an example of operation of an agricultural harvester for use in receiving a topographical map during a harvesting operation, detecting machine characteristics, and generating a function prediction map for use in presenting or controlling the agricultural harvester.

Fig. 6 is a block diagram illustrating one example of an agricultural harvester in communication with a remote server environment.

Fig. 7-9 illustrate examples of mobile devices that may be used in agricultural harvesters according to some examples of the present disclosure.

Fig. 10 is a block diagram illustrating one example of a computing environment usable in the agricultural harvester and architecture shown in the previous figures.

Detailed Description

For the purposes of promoting an understanding of the principles of the disclosure, reference will now be made to the examples illustrated in the drawings 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 thereby intended. Any alterations and further modifications in the described devices, systems, and methods, and any further applications of the principles of the disclosure as described herein are contemplated as would normally occur to one skilled in the art to which the disclosure relates. In particular, it is fully contemplated that the features, components, and/or steps described with respect to one example may be combined with the features, components, and/or steps described with respect to other examples of the present disclosure.

The present description relates to the use of field data acquired simultaneously with agricultural operations in combination with prior data to generate prediction maps (more specifically, prediction machine characteristic maps). In some examples, the predicted machine map may be used to control an agricultural work machine (e.g., an agricultural harvester). As described above, the performance of an agricultural harvester may deteriorate when the agricultural harvester engages a terrain feature, such as a slope. For example, if an agricultural harvester is going up a hill, power demand increases and machine performance may decline. This problem may be exacerbated when the soil is wet (e.g., shortly after rainfall) and the tires or tracks are subject to increased slip. In addition, the performance of the harvester (or other agricultural machine) may be adversely affected based on the topography of the field. For example, when passing a slope, the terrain may cause the machine to roll a certain amount. Without limitation, machine pitch or roll can affect the stability of the machine, internal material distribution, spray pressure on the sprayer, and the like. For example, grain loss can be affected by the topographical characteristics that cause the agricultural harvester 100 to pitch or roll. Increased pitch can result in faster grain being expelled from the rear, decreased pitch can keep grain in the machine, rolling elements can overload the sides of the grain cleaning system and cause more grain loss on those sides. Similarly, grain quality can be affected by both pitch and roll, and similar to grain loss, pitch or roll based reactions of materials other than grain left in or out of the machine can affect quality output. In another example, a terrain characteristic affecting pitch will have an effect on the clutter margin into the clutter system, thus affecting the clutter sensor output. The pitch considerations and the time at this level may be related to how much the margin increases and may be used for estimation when control is needed to predict the level and make adjustments.

The terrain map graphically maps ground heights across different geographic locations in the field of interest. Since the ground slope indicates a change in elevation, having two or more elevation values allows the slope to be calculated across an area having known elevation values. A larger size of the slope can be achieved by more regions with known height values. As the agricultural harvester travels across the terrain in a known direction, pitch and roll of the agricultural harvester can be determined based on the grade of the ground (i.e., the area of varying elevation). The terrain characteristics mentioned below may include, but are not limited to, height, grade (e.g., including machine orientation relative to grade), and ground profile (e.g., roughness).

Accordingly, the present discussion is directed to a system that receives a topographical map of a field during a harvesting operation and also uses field sensors to detect values indicative of one or more of internal material distribution, power characteristics, ground speed, grain loss, clutter, grain quality, or another machine characteristic. The system generates a model that models a relationship between terrain characteristics derived from the terrain map and output values from the field sensors. The model is used to generate a functional predictive machine map that predicts, for example, the power used at different locations in a field. The function-predictive machine map generated during the harvesting operation may be used to automatically control the harvester during the harvesting operation. In some cases, the function-predictive machine maps are used to generate a mission or path plan for an agricultural harvester operating in a field, for example, to improve power utilization, speed, or uniformity of internal material distribution throughout the operation. Of course, internal material distribution, power characteristics, ground speed, grain loss, clutter, and grain quality are merely examples of machine characteristics that may be predicted based on topographical characteristics, and other machine characteristics may also be predicted and used to control the machine.

Fig. 1 is a partially pictorial, partially schematic view of a self-propelled agricultural harvester 100. In the example shown, the agricultural harvester 100 is a combine harvester. Further, while a combine is provided as an example throughout this disclosure, it will be understood that the present description is also applicable to other types of harvesters, such as cotton harvesters, sugar cane harvesters, self-propelled forage harvesters, windrowers, or other agricultural work machines. Accordingly, the present disclosure is intended to encompass the various types of harvesters described and is therefore not limited to combine harvesters. Further, the present disclosure relates to other types of work machines, such as agricultural planters and sprinklers, construction equipment, forestry equipment, and turf management equipment, for which the generation of the prediction graph is applicable. Accordingly, the present disclosure is intended to encompass these various types of harvesters and other work machines, and is therefore not limited to combine harvesters.

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

The thresher 110 illustratively includes a threshing rotor 112 and a set of recesses 114. In addition, the agricultural harvester 100 also includes a separator 116. The agricultural harvester 100 also includes a grain cleaning subsystem or cleaning room 118 (collectively referred to as the grain cleaning subsystem 118) that includes a grain cleaning fan 120, a chaffer screen 122, and a screen 124. The material handling subsystem 125 also includes a discharge agitator 126, a trash elevator 128, a clean grain elevator 130, and a discharge auger 134 and spout 136. The clean grain elevator moves the clean grain into the clean grain tank 132. The agricultural harvester 100 also includes a residue subsystem 138, which may include a chopper 140 and a spreader 142. Agricultural harvester 100 also includes a propulsion subsystem that includes an engine that drives ground engaging components 144 (e.g., wheels or tracks). In some examples, a combine within the scope of the present disclosure may have more than one of any of the subsystems described above. In some examples, the agricultural harvester 100 can have left and right grain cleaning subsystems, separators, and the like, not shown in fig. 1.

In operation, as an overview, the agricultural harvester 100 is illustratively moved across a field in the direction indicated by arrow 147. As the agricultural harvester 100 moves, the header 102 (and associated reel 164) engages the crop to be harvested and collects the crop toward the cutter 104. The operator of the agricultural harvester 100 can be a local human operator, a remote human operator, or an automated system. An operator of agricultural harvester 100 may determine one or more of a height setting, a tilt angle setting, or a roll angle setting of header 102. For example, an operator inputs settings to a control system that controls the actuator 107 (described in more detail below). The control system may also receive settings from an operator for establishing the tilt angle and roll angle of header 102, and implement the entered settings by controlling associated actuators (not shown) that operate to change the tilt angle and roll angle of header 102. The actuator 107 maintains the header 102 at an elevation above the ground 111 based on the elevation setting, and where applicable, at a desired inclination and roll angle. Each of the height, roll, and tilt settings may be implemented independently of the other settings. The control system responds to header errors (e.g., differences between the height setting and the measured height of header 104 above ground 111 and, in some cases, bank angle and roll angle errors) with a responsiveness determined based on the sensitivity level. If the sensitivity level is set at a greater sensitivity level, the control system responds to smaller header position errors and attempts to reduce the detected error more quickly than if the sensitivity is at a lower sensitivity level.

Returning to the description of the operation of the agricultural harvester 100, after the crop is cut by the cutter 104, the severed crop material is moved in the feeder housing 106 by the conveyor toward the feed accelerator 108, which the feed accelerator 108 accelerates the crop material into the thresher 110. The crop is threshed by the rotor 112 rotating the crop material against the recess 114. The separator rotor moves the threshed crop in the separator 116, with the discharge agitator 126 moving a portion of the residue toward the residue subsystem 138. The portion of the residue delivered to the residue subsystem 138 is shredded by the residue shredder 140 and dispersed in 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 may include a weed seed ejector (not shown), such as a seed bagging machine or other seed collector or a seed shredder or other seed disruptor.

The grain falls into the clean grain subsystem 118. The chaffer screen 122 separates some of the larger material from the grain and the screen 124 separates some of the fine material from the clean grain. The clean grain falls onto an auger that moves the grain to the inlet end of clean grain elevator 130 and clean grain elevator 130 moves the clean grain upward, depositing the clean grain in clean grain tank 132. The residue is removed from the cleaning subsystem 118 by the airflow generated by the cleaning fan 120. The grain cleaning fan 120 directs air along an airflow path upwardly through the screen and chaffer. The airflow conveys the residue back in the agricultural harvester 100 toward the residue handling subsystem 138.

The trash lift 128 returns the trash to the thresher 110 where it is re-threshed. Alternatively, the trash can also be transferred by the trash elevator or another transport device to a separate threshing mechanism, where the trash is also threshed again.

Fig. 1 also shows that in one example, 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 may be in the form of a stereo camera or a monocular camera), and one or more loss sensors 152 disposed in the grain cleaning subsystem 118.

The ground speed sensor 146 senses the speed of travel of the agricultural harvester 100 on the ground. The ground speed sensor 146 may sense the travel speed of the agricultural harvester 100 by sensing the rotational speed of ground engaging components (e.g., wheels or tracks), drive shafts, axles, or other components. In some cases, a positioning system may be used to sense the speed of travel, such as a Global Positioning System (GPS), a dead reckoning system, a remote navigation (LORAN) system, a doppler velocity sensor, or various other systems or sensors that provide an indication of the speed of travel. The ground speed sensors 146 may also include direction sensors such as compasses, magnetometers, gravity sensors, gyroscopes, GPS derived, to determine direction of travel in two or three dimensions in combination with speed. As such, when the agricultural harvester 100 is on an incline, the orientation of the agricultural harvester 100 relative to the incline is known. For example, the orientation of the agricultural harvester 100 can include ascending a slope, descending a slope, or traversing a slope. When referred to in this disclosure, machine or ground speed may also include two-dimensional or three-dimensional directions of travel.

Loss sensor 152 illustratively provides an output signal indicative of the amount of grain loss occurring in both the right and left sides of the cleaning subsystem 118. In some examples, the sensor 152 is an impact sensor that counts impacts of grain per unit time or per unit distance traveled to provide an indication of grain loss occurring at the grain cleaning subsystem 118. The impact sensors on the right and left sides of the food cleaning subsystem 118 may provide separate signals or a combined or aggregate signal. In some examples, the sensor 152 may comprise a single sensor as opposed to providing separate sensors for each of the food cleaning subsystems 118.

Separator loss sensors 148 provide signals indicative of grain loss in the left and right separators (not separately shown in fig. 1). Separator loss sensors 148 may be associated with the left and right separators and may provide individual grain loss signals or a combined or aggregate signal. In some cases, various different types of sensors may also be used to sense grain loss in the separator.

Agricultural harvester 100 may 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 header 102 above ground 111; a stability sensor that senses oscillations or jerks (and amplitude) of the agricultural harvester 100; a residue setting sensor configured to sense whether agricultural harvester 100 is configured to shred residues, pile, etc.; a cleanroom fan speed sensor that senses the speed of the fan 120; a recess gap sensor that senses a gap between the rotor 112 and the recess 114; a threshing rotor speed sensor that senses the rotor speed of the rotor 112; a chaffer gap sensor which senses the size of the openings in the chaffer 122; a screen gap sensor that senses the size of the opening in the screen 124; a Material Outside Grain (MOG) humidity sensor that senses a humidity level of the MOG passing through the agricultural harvester 100; one or more machine setting sensors configured to sense various configurable settings of agricultural harvester 100; a machine orientation sensor that senses the orientation of agricultural harvester 100; and a crop property sensor that senses various different types of crop properties, such as crop type, crop moisture, and other crop properties. The crop property sensor may also be configured to sense a characteristic of the severed crop material when the agricultural harvester 100 is processing crop material. For example, in some cases, a crop property sensor may sense: grain quality, e.g. crushed grain, MOG content; grain components such as starch and protein; and grain feed rate as the grain passes through the feeder housing 106, the clean grain elevator 130, or elsewhere in the agricultural harvester 100. The crop property sensor may also sense the feed rate of the biomass through the feeder housing 106, the separator 116, or elsewhere in the agricultural harvester 100. The crop property sensor may also sense the feed rate as a mass flow rate of grain through the elevator 130 or through other portions of the agricultural harvester 100 or provide other output signals indicative of other sensed variables.

Examples of sensors for detecting or sensing power characteristics include, but are not limited to, voltage sensors, current sensors, torque sensors, hydraulic flow sensors, force sensors, bearing load sensors, and rotation sensors. The power characteristics may be measured at varying levels of granularity. For example, power usage may be sensed within the machine, within the subsystem, or by various components of the subsystem.

Examples of sensors for detecting internal material distribution include, but are not limited to, one or more cameras, capacitive sensors, electromagnetic or ultrasonic time-of-flight reflection sensors, signal attenuation sensors, weight or mass sensors, material flow sensors, and the like. These sensors may be placed at one or more locations in agricultural harvester 100 to sense the distribution of material in agricultural harvester 100 during operation of agricultural harvester 100.

Examples of sensors for detecting or sensing pitch or roll of the agricultural harvester 100 include accelerometers, gyroscopes, inertial measurement units, gravity sensors, magnetometers, and the like. These sensors may also indicate the grade of the terrain in which the agricultural harvester 100 is currently located.

Before describing how the agricultural harvester 100 generates the function-predicted machine map and uses the function-predicted machine map for control, a brief description of some items on the agricultural harvester 100 and its operation will be described first. The description of fig. 2 and 3 describes receiving a prior information map of the general type and combining information from the prior information map with a geo-referenced sensor signal generated by a field sensor, where the sensor signal is indicative of a characteristic in a field, such as a characteristic of a crop or weed present in the field. Characteristics of a "field" may include (but are not limited to): characteristics of the field, such as slope, weed density, weed type, soil moisture, surface quality; characteristics of crop properties, such as crop height, crop moisture, crop density, crop status; characteristics of grain properties, such as grain moisture, grain size, grain test weight; and characteristics of machine performance such as loss levels, quality of operation, fuel consumption, and power utilization. Relationships between characteristic values obtained from the field sensor signals and previous information map values are identified, and the relationships are used to generate a new function prediction map. The functional prediction map predicts values at different geographic locations in the field, and one or more of those values may be used to control the machine. In some cases, the function prediction map may be presented to a user, such as an operator of an agricultural work machine (which may be an agricultural harvester). The function prediction graph may be presented to the user visually (e.g., via a display), tactilely, or audibly. The user may interact with the function prediction graph to perform editing operations and other user interface operations. In some cases, the function prediction maps may be used to control agricultural work machines (e.g., agricultural harvesters), presented to operators or other users, and presented to operators or users for operator or user interaction.

After describing the general method with reference to fig. 2 and 3, a more specific method of generating a function prediction map that may be presented to an operator or user or used to control the agricultural harvester 100 or both is described with reference to fig. 4 and 5. Also, while the present discussion is directed to agricultural harvesters (particularly, combine harvesters), the scope of the present disclosure encompasses other types of agricultural harvesters or other agricultural work machines.

Fig. 2 is a block diagram illustrating portions of an example agricultural harvester 100. Fig. 2 shows that the agricultural harvester 100 illustratively includes one or more processors or servers 201, a data storage 202, a geographic position sensor 204, a communication system 206, and one or more field sensors 208 that sense one or more agricultural characteristics of a field concurrently with harvesting operations. Agricultural characteristics may include any characteristic that may have an effect on harvesting operations. Some examples of agricultural characteristics include characteristics of the harvesting machine, the field, the plants on the field, and the weather. Other types of agricultural characteristics are also included. The field sensor 208 generates a value corresponding to the sensed characteristic. 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 area generator 213, a control system 214, one or more controllable subsystems 216, and an operator interface mechanism 218. Agricultural harvester 100 can also include various other agricultural harvester functions 220. For example, the field sensors 208 include on-board sensors 222, remote sensors 224, and other sensors 226 that sense characteristics of the field during the course of an agricultural operation. Predictive model generator 210 illustratively includes a previous information variable versus field variable model generator 228, and predictive model generator 210 may include other items 230. The control system 214 includes a communication system controller 229, an operator interface controller 231, a setup controller 232, a path plan controller 234, a feed rate controller 236, a header and reel controller 238, a draper belt controller 240, a cover plate position controller 242, a residue system controller 244, a machine cleaning controller 245, a zone controller 247, and the system 214 may include other items 246. The controllable subsystems 216 include a machine and header actuator 248, a propulsion subsystem 250, a steering subsystem 252, a residue subsystem 138, a machine cleaning subsystem 254, and the subsystems 216 may include various other subsystems 256.

Fig. 2 also shows that the agricultural harvester 100 can receive the prior information map 258. As described below, for example, the previous map information map 258 includes a terrain map from previous operations in the field (e.g., an unmanned aerial vehicle performing a range scan operation from a known altitude), a terrain map sensed by an aircraft, a terrain map sensed by a satellite, a terrain map sensed by a ground vehicle (e.g., a GPS equipped planter), and so forth. However, the prior map information may also encompass other types of data obtained prior to the harvesting operation or maps from prior operations. For example, the topographical map may be retrieved from a remote source such as the United States Geological Survey (USGS). Fig. 2 also shows 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 mechanisms 218 may include joysticks, steering wheels, linkages, pedals, buttons, dials, keypads, user actuatable elements on user interface display devices (e.g., icons, buttons, etc.), microphones and speakers (where speech recognition and speech synthesis are provided), as well as various other types of control devices. Where a touch sensitive display system is provided, the operator 260 may utilize touch gestures to interact with the operator interface mechanism 218. The above examples are provided as illustrative examples and are not intended to limit the scope of the present disclosure. Accordingly, other types of operator interface mechanisms 218 may be used and are within the scope of the present disclosure.

Using communication system 206 or otherwise, prior information map 258 may be downloaded onto agricultural harvester 100 and stored in data storage device 202. In some examples, the communication system 206 may be a cellular communication system, a system that communicates via a wide area network or a local area network, a system that communicates via a near field communication network, or a communication system configured to communicate via any of a variety of other networks or combinations of networks. The communication system 206 may also include a system that facilitates the downloading or transfer of information to and from a Secure Digital (SD) card or a Universal Serial Bus (USB) card or both.

The geographic position sensor 204 illustratively senses or detects the geographic position of the agricultural harvester 100. The geolocation sensor 204 may include, but is not limited to, a Global Navigation Satellite System (GNSS) receiver that receives signals from a GNSS satellite transmitter. The geographic position sensor 204 may also include a real-time kinematic (RTK) assembly configured to enhance the accuracy of position data derived from GNSS signals. The geographic position sensor 204 may include a dead reckoning system, a cellular triangulation system, or any of a variety of other geographic position sensors.

The field sensor 208 may be any of the sensors described above with reference to fig. 1. The field sensor 208 includes an on-board sensor 222 mounted on the agricultural harvester 100. For example, the sensors may include a speed sensor (e.g., GPS, speedometer, or compass), an image sensor inside the agricultural harvester 100 (e.g., a clean grain camera or a camera mounted to identify material distribution in the agricultural harvester 100 (e.g., in a residue subsystem or a clean grain system)), a grain loss sensor, a clutter characteristic sensor, and a grain quality sensor. The field sensors 208 also include remote field sensors 224 that capture field information. The field data includes data acquired from sensors on the harvester or acquired by any sensor in the event that data is detected during a harvesting operation.

The predictive model generator 210 generates a model indicative of the relationship between the values sensed by the field sensors 208 and the characteristics mapped to the field by the prior information map 258. For example, if the previous information map 258 maps terrain characteristics for different locations in a field and the field sensors 208 are sensing values indicative of power usage, the previous information variables generate a predictive machine model for the field variable model generator 228 that models the relationship between terrain characteristics and power usage. A predictive machine model may also be generated based on the terrain characteristics from the previous information map 258 and the plurality of field data values generated by the field sensors 208. The predictive machine model generated by the predictive model generator 210 is then used by the predictive map generator 212 to generate a functional predictive machine characterization map that predicts values of machine characteristics (e.g., internal material distribution) sensed by the field sensors 208 at different locations in the field based on the prior information map 258.

In some examples, the types of values in function prediction graph 263 may be the same as the types of field data sensed by field sensor 208. In some cases, the types of values in function prediction graph 263 may have different units than the data sensed by field sensor 208. In some examples, the types of values in function prediction graph 263 may be different from, but related to, the type of data sensed by field sensor 208. For example, in some examples, the type of data sensed by the field sensors 208 may indicate the type of values in the function prediction graph 263. In some examples, the type of data in function prediction graph 263 may be different from the type of data in prior information graph 258. In some cases, the type of data in function prediction graph 263 may have different units than the data in previous information graph 258. In some examples, the type of data in the function prediction graph 263 may be different from the type of data in the previous information graph 258, but have a relationship to the type of data in the previous information graph 258. For example, in some examples, the type of data in the prior information graph 258 may indicate the type of data in the function prediction graph 263. In some examples, the type of data in function prediction graph 263 is different from one or both of the type of field data sensed by field sensors 208 and the type of data in prior information graph 258. In some examples, the type of data in function prediction graph 263 is the same as one or both of the type of field data sensed by field sensors 208 and the type of data in prior information graph 258. In some examples, the type of data in function prediction graph 263 is the same as one of the type of field data sensed by field sensors 208 or the type of data in prior information graph 258, and is different from the other.

The prediction map generator 212 may use the terrain characteristics in the previous information map 258 and the model generated by the predictive model generator 210 to generate a functional prediction map 263 that predicts machine characteristics for different locations in the field. The prediction graph generator 212 thus outputs the prediction graph 264.

As shown in fig. 2, the prediction graph 264 predicts the values of the sensed characteristics (sensed by the field sensors 208) or characteristics related to the sensed characteristics at various locations across the field based on the previous information values at those locations in the previous information graph 258 and using a prediction model. For example, if the prediction model generator 210 has generated a prediction model that indicates a relationship between terrain characteristics and power usage, then given the terrain characteristics at different locations across the field, the prediction map generator 212 generates a prediction map 264 that predicts values of power usage at different locations across the field. The prediction map 264 is generated using the topographical characteristics at those locations obtained from the topographical map and the relationship between the topographical characteristics and machine characteristics obtained from the prediction model. The control system may use the predicted power usage to adjust, for example, the power distribution among the engine throttle or various subsystems to meet the predicted power usage requirements.

Some variations of the types of data mapped in the previous information graph 258, the types of data sensed by the field sensors 208, and the types of data predicted on the prediction graph 264 will now be described.

In some examples, the type of data in the previous information graph 258 is different from the type of data sensed by the field sensor 208, while the type of data in the prediction graph 264 is the same as the type of data sensed by the field sensor 208. For example, the prior information map 258 may be a topographical map, and the variable sensed by the field sensors 208 may be a machine characteristic. The predicted map 264 may then be a predicted machine map that maps the predicted machine characteristic values to different geographic locations in the field.

Additionally, in some examples, the type of data in the previous information graph 258 is different from the type of data sensed by the field sensor 208, and the type of data in the prediction graph 264 is different from both the type of data in the previous information graph 258 and the type of data sensed by the field sensor 208. For example, the prior information map 258 may be a topographical map, and the variable sensed by the site sensor 208 may be machine pitch/roll. The prediction graph 264 may then be a predicted internal distribution graph that maps the predicted internal distribution values to different geographic locations in the field.

In some examples, the previous information graph 258 is from a previous operation through the field and the data type is different from the data type sensed by the field sensors 208, while the data type in the prediction graph 264 is the same as the data type sensed by the field sensors 208. For example, the prior information map 258 may be a map of seed populations generated during planting, and the variable sensed by the in situ sensor 208 may be stem size. Predicted map 264 may then be a predicted stem size map that maps the predicted stem size values to different geographic locations in the field. In another example, the prior information map 258 may be a seeding mix map and the variable sensed by the field sensor 208 may be a crop condition such as an upright crop or a lodging crop. The prediction graph 264 can then be a predicted crop state graph that maps predicted crop state values to different geographic locations in the field.

In some examples, the previous information graph 258 is from a previous operation through the field and the data type is the same as the data type sensed by the field sensors 208, and the data type in the prediction graph 264 is also the same as the data type sensed by the field sensors 208. For example, the prior information map 258 may be a yield map generated in the previous year, and the variable sensed by the site sensor 208 may be yield. The prediction graph 264 may then be a predicted yield graph that maps predicted yield values to different geographic locations in the field. In such an example, the predictive model generator 210 may use the relative yield differences in the geo-referenced previous information map 258 from the previous year to generate a predictive model that models the relationship between the relative yield differences on the previous information map 258 and the yield values sensed by the field sensors 208 during the current harvesting operation. The prediction graph generator 210 then uses the prediction model to generate a predicted yield graph.

In some examples, the prediction graph 264 may be provided to the control region generator 213. The control region generator 213 groups the continuous individual dot data values on the prediction map 264 into control regions. The control region may comprise two or more consecutive portions of a region (e.g. a field) where the control parameter is constant corresponding to the control region for controlling the controllable subsystem. For example, the response time to change the settings of controllable subsystem 216 may not be sufficient to satisfactorily respond to changes in the values contained in a graph, such as prediction graph 264. In this case, control region generator 213 parses the map and identifies control regions of defined size to accommodate the response time of controllable subsystem 216. In another example, the control region may be sized to reduce wear caused by excessive actuator movement resulting from continuous adjustment. In some examples, there may be different sets of control regions for each controllable subsystem 216 or groups of controllable subsystems 216. The control region may be added to the prediction map 264 to obtain the prediction control region map 265. Thus, the predicted control region map 265 may be similar to the predicted map 264, except that the predicted control region map 265 includes control region information defining control regions. Thus, as described herein, function prediction graph 263 may or may not include a control region. Both the prediction map 264 and the prediction control area map 265 are function prediction maps 263. In one example, the function prediction graph 263 does not include a control region (e.g., prediction graph 264). In another example, the function prediction graph 263 does include a control region (e.g., the predicted control region graph 265). In some examples, if an intercropping production system is implemented, multiple crops may be present in a field at the same time. In this case, prediction map generator 212 and control area generator 213 can identify the location and characteristics of two or more crops and then generate prediction map 264 and prediction control area map 265 accordingly.

It will also be appreciated that the control region generator 213 may cluster values to generate control regions, and that the control regions may be added to the predicted control region map 265 or a separate map displaying only the generated control regions. In some examples, the control area may be used only to control or calibrate the agricultural harvester 100 or both. In other examples, the control area may be presented to the operator 260 and used to control or calibrate the agricultural harvester 100, and in other examples, the control area may be presented only to the operator 260 or another user or stored for later use.

Either the prediction map 264 or the prediction control area map 265, or both, are provided to the control system 214, and the control system 214 generates control signals based on either the prediction map 264 or the prediction control area map 265, or both. In some examples, the communication system controller 229 controls the communication system 206 to communicate the predicted map 264 or the predicted control area map 265 or control signals based on the predicted map 264 or the predicted control area map 265 to other agricultural harvesters that are harvesting in the same field. In some examples, the communication system controller 229 controls the communication system 206 to transmit the prediction graph 264, the prediction control area graph 265, or both to other remote systems.

In some examples, prediction graph 264 may be provided to route/task generator 267. Route/mission generator 267 maps a travel path that agricultural harvester 100 travels during a harvesting operation based on prediction graph 264. The travel path may also include machine control settings corresponding to locations along the travel path. For example, if the travel path goes up a hill, the point before the hill rises, the travel path may include controls that direct power to the propulsion system to maintain the speed or feed rate of the agricultural harvester 100. In some examples, route/mission generator 267 analyzes different orientations of agricultural harvester 100 for a plurality of different travel routes and predicts predicted machine characteristics generated by the orientation from prediction graph 264, and selects a route with a desirable outcome (e.g., rapid harvest time or desired power utilization or material distribution uniformity).

The operator interface controller 231 is operable to generate control signals to control the operator interface mechanism 218. The operator interface controller 231 is also operable to present the prediction graph 264 or the prediction control area graph 265, or other information derived from or based on the prediction graph 264, the prediction control area graph 265, or both, to the operator 260. The operator 260 may be a local operator or a remote operator. As an example, the controller 231 generates a control signal to control a display mechanism to display one or both of the prediction graph 264 and the prediction control region graph 265 to the operator 260. The controller 231 may generate an operator actuatable mechanism that is displayed and actuatable by an operator to interact with the displayed map. The operator can edit the map by, for example, correcting the power utilization displayed on the map based on the operator's observations. The settings controller 232 may generate control signals to control various settings on the agricultural harvester 100 based on the prediction graph 264, the prediction control area graph 265, or both. For example, the setup controller 232 may generate control signals that control the machine and the header actuators 248. In response to the generated control signals, the machine and header actuators 248 operate to control one or more of, for example, screen and chaff settings, thresher clearance, rotor settings, clean grain fan speed settings, header height, header function, reel speed, reel position, draper function (where the agricultural harvester 100 is coupled to a draper header), grain header function, internal 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. Path planning controller 234 may control the path planning system to generate a route for agricultural harvester 100, and may control propulsion subsystem 250 and steering subsystem 252 to steer agricultural harvester 100 along the route. Feed rate controller 236 may control various subsystems, such as propulsion subsystem 250 and machine actuators 248, to control the feed rate based on prediction map 264 or prediction control region map 265, or both. For example, as the agricultural harvester 100 approaches a decline terrain having an estimated speed value above a selected threshold, the feed rate controller 236 may decrease the speed of the machine 100 to maintain a constant feed rate of biomass through the agricultural harvester 100. Header and reel controller 238 may generate control signals to control header even reel or other header functions. The draper belt controller 240 may generate control signals to control draper belts or other draper functions based on the prediction map 264, the prediction control area map 265, or both. For example, as agricultural harvester 100 approaches a decline terrain having an estimated speed value above a selected threshold, draper belt controller 240 may increase the draper belt speed to prevent material from jamming on the belt. The coverplate position controller 242 may generate control signals to control the position of a coverplate included on the header based on the prediction map 264 or the predictive control area map 265, or both, and the residual system controller 244 may generate control signals to control the residual subsystem 138 based on the prediction map 264 or the predictive control area map 265, or both. The machine purge controller 245 may generate control signals to control the machine purge subsystem 254. For example, when the agricultural harvester 100 is to traverse on an incline such that it is estimated that the internal material distribution will be disproportionately to one side of the cleaning subsystem 254, the machine cleaning controller 245 may adjust the cleaning subsystem 254 to account for or correct the disproportionate material. Other controllers included on the agricultural harvester 100 may also control other subsystems based on the predicted map 264 or the predicted control area map 265, or both.

Fig. 3A and 3B (collectively referred to herein as fig. 3) illustrate a flow chart illustrating one example of the operation of the agricultural harvester 100 in generating the prediction graph 264 and the prediction control area graph 265 based on the previous information graph 258.

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 described above, the prior information map 258 maps the values of the variables corresponding to the first characteristic to different locations in the field, as indicated by block 282. As indicated at block 281, receiving the prior information map 258 may involve selecting one or more of a plurality of possible prior information maps that are available. For example, one prior information map may be a relief profile map generated from aerial phase profilometry images. Another prior information map may be a map generated during a prior pass through the field, which may be performed by a different machine (e.g., a sprinkler or other machine) performing a prior operation in the field. The process of selecting one or more prior information graphs may be manual, semi-automatic, or automatic. The prior information map 258 is based on data collected prior to the current harvesting operation. This is indicated by block 284. For example, data may be collected by a GPS receiver mounted on the apparatus during previous field operations. For example, data may be collected during a lidar range scanning operation during the previous year or earlier in the current growing season or at other times. The data may be based on data detected or received in a manner other than using a lidar range scan. For example, a drone equipped with a fringe projection profile measurement system may detect the profile or height of the terrain. Or, for example, some topographical features may be estimated based on weather patterns, such as rutting due to erosion or clumping during freeze-thaw cycles. In some examples, the prior information graph 258 may be created by combining data from several sources, such as those listed above. Or, for example, data of the prior information map 258 (e.g., a topographical map) can be sent to the agricultural harvester 100 using the communication system 206 and stored in the data storage device 202. The data of the prior information map 258 may also be provided to the agricultural harvester 100 in other ways using the communication system 206, which is indicated by block 286 in the flow chart of fig. 3. In some examples, the prior information map 258 may be received by the communication system 206.

At the beginning of a harvesting operation, the field sensors 208 generate sensor signals indicative of one or more field data values indicative of machine characteristics such as power usage, machine speed, internal material distribution, grain loss, clutter, or grain quality. Examples of in-situ sensor 288 are discussed with reference to blocks 222, 290 and 226. As explained above, the field sensor 208 includes: an on-board sensor 222; a remote field sensor 224, such as a UAV based sensor that flies once to collect field data, is shown at block 290; or other type of field sensor as designated by the field sensor 226. In some examples, data from onboard sensors is geo-referenced using position, heading, or speed data from geo-location sensor 204.

Predictive model generator 210 controls the previous information variables versus field variable model generator 228 generates a model that models the relationship between the mapped values contained in previous information maps 258 and the field values sensed by field sensors 208, as indicated by block 292. The characteristics or data types represented by the mapped values in the previous information map 258 and the field values sensed by the field sensors 208 may be the same characteristics or data types or different characteristics or data types.

The relationships or models generated by the predictive model generator 210 are provided to a predictive graph generator 212. The prediction map generator 212 uses the prediction model and the prior information map 258 to generate a prediction map 264 that predicts values of the characteristic sensed by the field sensors 208 or a different characteristic related to the characteristic sensed by the field sensors 208 at different geographic locations in the field being harvested, as indicated by block 294.

It should be noted that in some examples, prior information map 258 may include two or more different maps or two or more different layers of a single map. Each of the two or more different maps or each of the two or more different layers of a single map maps a different type of variable to a geographic location in the field. In such an example, predictive model generator 210 generates a predictive model that models the relationship between the field data and various different variables mapped by two or more different maps or two or more different map layers. Similarly, the field sensors 208 may include two or more sensors that each sense a different type of variable. Accordingly, predictive model generator 210 generates a predictive model that models the relationship between the various types of variables mapped by prior information map 258 and the various types of variables sensed by field sensors 208. The prediction map generator 212 may use the prediction model and various maps or layers in the previous information map 258 to generate a functional prediction map 263 that predicts values of various sensed characteristics (or characteristics related to the sensed characteristics) sensed by the field sensors 208 at different locations in the field being harvested.

Predictive graph generator 212 configures predictive graph 264 such that predictive graph 264 can be manipulated (or consumed) by control system 214. The prediction graph generator 212 may provide the prediction graph 264 to the control system 214 or the control area generator 213, or both. Some examples of different ways in which prediction graph 264 may be configured or output will be described with reference to blocks 296, 293, 295, 299 and 297. For example, prediction graph generator 212 configures prediction graph 264 such that prediction graph 264 includes values that can be read by control system 214 and used as a basis for generating control signals for one or more different controllable subsystems of agricultural harvester 100, as indicated by block 296.

Route/task generator 267 maps the path of travel that agricultural harvester 100 traveled during the harvesting operation based on prediction graph 204, as indicated by block 293. The control region generator 213 may divide the prediction graph 264 into control regions based on the values on the prediction graph 264. Geographically contiguous values within a threshold of each other may be grouped into control regions. The threshold may be a default threshold, or may be set based on operator input, based on input from an automated system, or based on other criteria. The size of the region may be based on the responsiveness of the control system 214, controllable subsystem 216, or based on wear considerations or other criteria, as indicated at block 295. The prediction graph generator 212 configures the prediction graph 264 for presentation to an operator or other user. The control region generator 213 may configure the predictive control region 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 graph 264 or the prediction control area graph 265, or both, may include one or more of a predicted value on the prediction graph 264 associated with a geographic location, a control area on the prediction control area graph 265 associated with a geographic location, and a set value or control parameter used based on the predicted value on the graph 264 or the area on the prediction control area graph 265. In another example, the presentation may include more abstract information or more detailed information. The presentation may also include a confidence level indicating that the predicted values on the prediction map 264 or the regions on the prediction control region map 265 conform to the accuracy of the measurements that may be measured by the sensors on the agricultural harvester 100 as the agricultural harvester 100 moves through the field. Further, in the event that information is presented to more than one location, an authentication or authorization system may be provided to implement the authentication and authorization process. For example, there may be a personal hierarchy that is authorized to view and change the information of the graph and other presentations. By way of example, the on-board display device may display the images in near real-time locally on the machine only, or the images may also be generated at one or more remote locations. In some examples, each physical display at each location may be associated with a human or user permission level. The user permission level may be used to determine which display elements are visible on the physical display device and which values a corresponding person may change. By way of example, a local operator of the machine 100 may not be able to see the information corresponding to the prediction graph 264 or make any changes to the machine operation. However, a supervisor at a remote location may be able to see the prediction graph 264 on the display, but may not be able to make changes. An administrator, who may be at a separate remote location, may be able to see all of the elements on prediction graph 264 and also change prediction graph 264 for machine control. This is one example of an authorization hierarchy that may be implemented. The prediction graph 264 or the prediction control area graph 265 or both may also be configured in other ways, as indicated by block 297.

At block 298, input from the geolocation sensor 204 and other field sensors 208 is received by the control system. Block 300 represents control system 214 receiving input from geo-location sensor 204 identifying the geographic location of agricultural harvester 100. Block 302 represents the control system 214 receiving sensor input indicative of a trajectory or heading of the agricultural harvester 100, and block 304 represents the control system 214 receiving a speed of the agricultural harvester 100. Block 306 represents the control system 214 receiving additional information from the various field sensors 208.

At block 308, control system 214 generates control signals to control controllable subsystem 216 based on predicted map 264 or predicted control area map 265 or both, as well as inputs from geolocation sensor 204 and any other field sensors 208. At block 310, the control system 214 applies the control signal to the controllable subsystem. It will be appreciated that the particular control signal generated and the particular controllable subsystem 216 being controlled may vary based on one or more different things. For example, the control signals generated and the controllable subsystem 216 controlled may be based on the type of prediction graph 264 or prediction control area graph 265, or both, being used. Similarly, the control signals generated, the controllable subsystems 216 controlled, and the timing of the control signals may be based on various delays in the flow of crop through the agricultural harvester 100 and the responsiveness of the controllable subsystems 216.

By way of example, the generated prediction graph 264 in the form of a prediction machine graph may be used to control one or more subsystems 216. For example, the predicted machine map may include machine speed values that are geo-coordinated with reference to locations within the field being harvested. Machine speed values from the predicted machine map may be extracted and used to control header and feeder housing speeds to ensure that the header 104 and feeder housing 106 can handle an increase in material engaged by the agricultural harvester 100 as it moves more quickly through the field. The foregoing examples of using a predicted machine map to relate to machine speed are provided as examples only. Accordingly, various other control signals may be generated to control the one or more controllable subsystems 216 using values obtained from a predicted machine map or other type of predicted map.

At block 312, it is determined whether the harvesting operation has been completed. If harvesting is not complete, the process proceeds to block 314 where field sensor data from the geo-location sensor 204 and the field sensor 208 (and possibly other sensors) are continuously read in block 314.

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 predictive maps 264, the predictive control area maps 265, the models generated by the predictive model generator 210, the areas generated by the control area generator 213, one or more control algorithms implemented by the controller in the control system 214, and other triggered learning.

The learning trigger criteria may 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 may involve recreating the relationships used to generate the predictive model when a threshold amount of field sensor data is obtained from the field sensors 208. In these examples, receipt of more than a threshold amount of field sensor data from field sensors 208 triggers or causes prediction model generator 210 to generate a new prediction model for use by prediction graph generator 212. Thus, as the agricultural harvester 100 continues harvesting operations, receipt of a threshold amount of field sensor data from the field sensors 208 triggers creation of a new relationship represented by the predictive model generated by the predictive model generator 210. Further, the new prediction graph 264, the predicted control region graph 265, or both may be regenerated using the new prediction model. Block 318 represents detecting a threshold amount of field sensor data for triggering the creation of a new predictive model.

In other examples, the learning trigger criteria may be based on how much the field sensor data from the field sensors 208 changed from a previous value or threshold. For example, if the change in the field sensor data (or the relationship between the field sensor data and the information in the previous information graph 258) is within a range, less than a defined amount, or below a threshold, then the predictive model generator 210 does not generate a new predictive model. As a result, the prediction map generator 212 does not generate the new prediction map 264, the prediction control area map 265, or both. However, if the change in the field sensor data is out of range or exceeds a predefined amount or threshold, for example, or if the relationship between the field sensor data and the information in the previous information map 258 changes by a defined amount, for example, the prediction model generator 210 generates a new prediction model using all or part of the newly received field sensor data that the prediction map generator 212 uses to generate the new prediction map 264. At block 320, a change in the field sensor data (e.g., a magnitude of a change in the amount by which the data exceeds a selected range or a magnitude of a change in the relationship between the field sensor data and the information in the previous information map 258) may be used as a trigger to cause a new predictive model and predictive map to be generated. The thresholds, ranges, and defined amounts may be set as default values, or by an operator or user through user interface interaction, or by an automated system, or otherwise.

Other learning triggering criteria may also be used. For example, if the prediction model generator 210 switches to a different previous information map (different from the originally selected previous information map 258), switching to the different previous information map may trigger the prediction model generator 210, prediction map generator 212, control region generator 213, control system 214, or other item to relearn. In another example, the transition of agricultural harvester 100 to a different terrain or a different control area may also be used as a learning trigger criteria.

In some cases, the operator 260 may also edit the prediction graph 264 or the prediction control area graph 265, or both. The editing may change the values on the prediction graph 264, or change the size, shape, location or presence of the control region 265, or predict the values on the control region graph 265, or both. Block 321 shows that the compiled information can be used as learning trigger criteria.

In some cases, the operator 260 may also observe that automatic control of the controllable subsystems is not desired by the operator. In these cases, the operator 260 may provide manual adjustments to the controllable subsystems, which reflect that the operator 260 desires the controllable subsystems to operate in a different manner than commanded by the control system 214. Thus, the manual alteration of settings by the operator 260 can result in the predictive model generator 210 relearning the model, the predictive map generator 212 regenerating the map 264, the control region generator 213 regenerating the control region on the predictive control region map 265, and the control system 214 relearning its control algorithm or performing machine learning on one of the controller components 232-246 in the control system 214 based on the adjustments by the operator 260 (as indicated at block 322). Block 324 represents using other triggered learning criteria.

In other examples, relearning may be performed periodically or intermittently, e.g., based on a selected time interval (e.g., a discrete time interval or a variable time interval). This is indicated by block 326.

As indicated at block 326, if relearning is triggered (whether based on learning trigger criteria or based on an elapsed time interval), one or more of the prediction model generator 210, prediction map generator 212, control region generator 213, and control system 214 performs machine learning to generate a new prediction model, a new prediction map, a new control region, and a new control algorithm, respectively, based on the learning trigger criteria. Any additional data collected since the last learning operation was performed is used to generate a new prediction model, a new prediction graph, and a new control algorithm. Performing relearning is indicated by block 328.

If the harvesting operation is complete, operation moves from block 312 to block 330, where in block 330 one or more of the prediction map 264, the prediction control region map 265, and the prediction model generated by the prediction model generator 210 are stored. Prediction graph 264, prediction control region graph 265, and prediction model may be stored locally on data storage device 202 or transmitted to a remote system for later use using communication system 206.

It will be noted that although some examples herein describe the predictive model generator 210 and predictive graph generator 212 receiving prior information graphs when generating the predictive model and functional predictive graph, respectively, in other examples, the predictive model generator 210 and predictive graph generator 212 may receive other types of graphs, including predictive graphs, such as functional predictive graphs generated during harvesting operations.

Fig. 4A is a block diagram of a portion of the agricultural harvester 100 shown in fig. 1. Specifically, FIG. 4A illustrates an example of prediction model generator 210 and prediction graph generator 212 in more detail, among others. FIG. 4A also shows the flow of information between the various components shown. Predictive model generator 210 receives terrain map 332 as a prior information map. Predictive model generator 210 also receives an indication of geographic location 334 or a geographic location from geographic location sensor 204. The field sensors 208 illustratively include machine sensors (e.g., machine sensors 336) and a processing system 338. In some cases, machine sensor 336 may be located on agricultural harvester 100. The processing system 338 processes the sensor data generated from the on-board machine sensors 336 to generate processed data, some examples of which are described below.

In some examples, machine sensor 336 may generate an electronic signal indicative of a characteristic sensed by machine sensor 336. Processing system 338 processes one or more sensor signals obtained via machine sensors 336 to generate processed data identifying one or more machine characteristics. The machine characteristics identified by the processing system 338 may include internal material distribution, power usage, power utilization, machine speed, wheel slip, and the like.

The site sensor 208 may be or include an optical sensor, such as a camera located in the agricultural harvester 100 (hereinafter "process camera") that views the interior of the agricultural harvester 100 processing the grain agricultural material. Thus, in some examples, the processing system 338 is operable to detect the internal distribution of agricultural material passing through the agricultural harvester 100 based on images captured by the machine sensor 208. For example, whether the agricultural material is unevenly distributed across the grain cleaning system may be due to machine roll or pitch.

In other examples, the field sensor 208 may be or include a GPS that senses machine position. In this case, the processing system 338 may also derive speed and direction from the sensor signals. In another example, the site sensors 208 may include one or more power sensors that detect individual or aggregate power characteristics of one or more subsystems on the agricultural harvester 100. In this case, the processing system 338 may aggregate or separate power characteristics through subsystems or machine components.

Other machine properties and sensors may also be used. In some examples, raw or processed data from machine sensors 336 may be presented to operator 260 via operator interface mechanism 218. The operator 260 may be on the agricultural harvester 100 or at a remote location.

As shown in fig. 4A, the example predictive model generator 210 includes one or more of a power characteristics versus terrain characteristics model generator 342, a machine speed versus terrain characteristics model generator 344, a material distribution versus terrain characteristics model generator 345, a grain loss versus terrain characteristics model generator 346, a miscellaneous versus terrain characteristics model generator 347, and a grain quality versus terrain characteristics model 348. In other examples, predictive model generator 210 may include more, fewer, or different components than those shown in the example of fig. 4A. Thus, in some examples, predictive model generator 210 may also include other items 349, which may include other types of predictive model generators, to generate other types of machine characterization models.

The present discussion is made with respect to an example in which machine sensor 336 is a power characteristic sensor (e.g., a hydraulic pressure sensor, a voltage sensor, etc.). It will be appreciated that these are just a few examples, as other examples of machine sensors 336 are also contemplated herein. Model generator 342 identifies a relationship between the power characteristic at a geographic location corresponding to processed data 340 and the value of the terrain characteristic at the same geographic location. The terrain characteristic value is a geographic reference value contained in the terrain map 332. The model generator 342 then generates a predictive machine model 350, which predictive machine model 350 is used by a power map generator 352 to predict power characteristics at a location in the field based on topographical characteristics of the location in the field. For example, the field sensors 208 sense power usage and the predictive graph generator 352 outputs estimated power usage requirements at various locations in the field.

The present discussion is made with respect to an example where machine sensor 336 is a machine speed sensor (e.g., a global positioning system device, a speedometer, a compass, etc.). It will be appreciated that these are just a few examples, as other examples of machine sensors 336 are also contemplated herein. Model generator 344 identifies a relationship between machine speed at a geographic location corresponding to processed sensor data 340 and a value of a terrain characteristic at the same geographic location. Likewise, the terrain characteristic value is a geographic reference value contained in the terrain map 332. The model generator 344 then generates a predictive machine model 350, which predictive machine model 350 is used by the machine speed map generator 354 to predict the machine speed at a location in the field based on the topographical property values of that location. For example, machine speed and direction are sensed by the field sensors 208, and the prediction map generator 354 outputs the machine speed and direction estimated at various locations in the field.

The present discussion is made with respect to an example in which machine sensor 336 is an optical sensor. It will be appreciated that this is but one example, and that the above-described sensors are also contemplated herein as other examples of machine sensors 336. The model generator 345 identifies the relationship between the material distribution detected in the processed data 340 at the geographic location corresponding to the obtained sensor data (e.g., the material distribution in the agricultural harvester 100 can be identified based on the data captured by the optical sensor) and the topographical characteristics from the topographical map 332 corresponding to the same location in the field in which the material distribution was detected. Based on the relationship established by the model generator 345, the model generator 345 generates a predictive machine model 350. The material distribution map generator 355 uses the predictive machine model 350 to predict the material distribution at different locations in the field based on the geo-referenced topographical features contained in the topographical map 332 at the same location in the field.

The present discussion is discussed with respect to an example where machine sensor 336 is a grain loss sensor. It will be appreciated that this is but one example, and that the above-described sensors are also contemplated herein as other examples of machine sensors 336. Model generator 346 identifies the relationship between grain loss detected in processed data 340 at a geographic location corresponding to the geolocation sensor data and the topographical features from topographical map 332 corresponding to the same location in the field of geo-located grain loss. Based on the relationship established by model generator 346, model generator 346 generates predictive machine model 350. The grain loss map generator 356 uses the predictive machine model 350 to predict grain loss at different locations in the field based on georeferenced topographical features contained in the topographical map 332 at the same location in the field.

The present discussion is made with respect to an example where machine sensor 336 is a miscellaneous sensor. It will be appreciated that this is but one example, and that the above-described sensors are also contemplated herein as other examples of machine sensors 336. The model generator 347 identifies the relationship between clutter detected in the processed data 340 at geographic locations corresponding to the geolocation sensor data and the topographical features of the topographical map 332 corresponding to the same locations in the field from the geolocation clutter features. Based on the relationship established by the model generator 347, the model generator 347 generates a predictive machine model 350. The clutter generator 357 uses the predictive machine model 350 to predict clutter characteristics at different locations in the field based on geo-referenced terrain characteristics contained in the terrain map 332 at the same location in the field.

The present discussion is made with respect to an example where machine sensor 336 is a grain quality sensor. It will be appreciated that this is but one example, and that the above-described sensors are also contemplated herein as other examples of machine sensors 336. Model generator 348 identifies a relationship between grain mass detected in processed data 340 at a geographic location corresponding to the geo-located sensor data and topographical features from topographical map 332 corresponding to the same location in a field of geo-located grain masses. Based on the relationship established by model generator 348, model generator 348 generates a predictive machine model 350. The grain quality map generator 358 uses the predictive machine model 350 to predict grain quality at different locations in the field based on georeferenced topographic characteristics contained in the topographic map 332 at the same location in the field.

Predictive model generator 210 is operable to generate a plurality of predictive machine models, such as one or more predictive machine models generated by

model generators

342, 344, and 345. In another example, two or more of the

predictive machine models

342, 344, and 345 described above may be combined into a single predictive machine model that predicts two or more machine characteristics, such as material distribution, power characteristics, clutter characteristics, grain loss, grain quality, and machine speed, based on topographical characteristics at different locations in a field. Any one or combination of these machine models is represented collectively by machine model 350 in FIG. 4A.

The predictive machine model 350 is provided to the predictive graph generator 212. In the example of FIG. 4A, prediction graph generator 212 includes a power characteristic graph generator 352, a machine speed graph generator 354, a material distribution graph generator 355, a grain loss graph generator 356, a clutter graph generator 357, and a grain quality graph generator 358. In other examples, prediction graph generator 212 may include more, fewer, or different graph generators. Thus, in some examples, prediction graph generator 212 may include other projects 359 that may include other types of graph generators to generate machine characteristic graphs for other types of machine characteristics.

The power characteristic map generator 352 receives the predictive machine model 350 that predicts power characteristics based on terrain characteristics from the terrain map 332 and generates a predictive map that predicts power characteristics for different locations in the field. For example, the predicted power characteristic may include a predicted required power.

The machine speed map generator 354 generates a prediction map that predicts the machine speeds for different locations in the field based on the machine speed values at those locations in the field and the predictive machine model 350.

The material profile generator 355 illustratively generates a material profile 360 that predicts the material distribution at different locations in the field based on the topographical characteristics of those locations and the predictive machine model 350.

The grain loss map generator 356 illustratively generates a grain loss map 360 that predicts grain loss at different locations in the field based on topographical characteristics of those locations and the predictive machine model 350.

The clutter generator 357 illustratively generates a clutter map 360 that predicts clutter characteristics for different locations in the field based on the terrain characteristics for those locations and the predictive machine model 350.

The grain mass map generator 358 illustratively generates a grain mass map 360 that predicts a property indicative of grain mass at different locations in the field based on topographical properties of those locations and the predictive machine model 350.

Prediction graph generator 212 outputs one or more predicted machine characteristic graphs 360 that are predictions of machine characteristics. Each predicted machine characteristic map 360 predicts a respective machine characteristic for a different location in the field. Each generated predicted machine characteristic map 360 may be provided to control region generator 213, control system 214, or both. The control region generator 213 generates control regions and incorporates those control regions into a function prediction map (i.e., prediction map 360) to generate a predicted control region map 265. One or both of the prediction map 264 and the prediction control region map 265 can be provided to the control system 214, and the control system 214 generates control signals to control the one or more controllable subsystems 216 based on the prediction map 264, the prediction control region map 265, or both.

Fig. 4B is a block diagram illustrating some examples of real-time (in-situ) sensors 208. Some of the sensors shown in fig. 4B, or different combinations thereof, may have both sensors 336 and processing system 338. Some of the possible in-situ sensors 208 shown in FIG. 4B are shown and described above with reference to previous figures and are similarly numbered. Fig. 4B illustrates that the field sensors 208 may include operator input sensors 980, machine sensors 982, harvest material property sensors 984, field and soil property sensors 985, environmental characteristic sensors 987, and they may include various other sensors 226. Non-machine sensors 983 include operator input sensor(s) 980, harvest material property sensor(s) 984, field and soil property sensor(s) 985, environmental property sensor(s) 987, and may also include other sensors 226. The operator input sensor 980 may be a sensor that senses operator input through the operator interface mechanism 218. Thus, the operator input sensor 980 may sense user movement of a link, joystick, steering wheel, button, dial, or pedal. The operator input sensor 980 may also sense user interaction with other operator input mechanisms (e.g., with a touch-sensitive screen, with a microphone utilizing speech recognition, or any of a variety of other operator input mechanisms).

Machine sensor 982 may sense different characteristics of agricultural harvester 100. For example, as described above, the machine sensors 982 may include the machine speed sensor 146, the separator loss sensor 148, the clean grain camera 150, the forward looking image capture mechanism 151, the loss sensor 152, or the geographic position sensor 204, examples of which are described above. The machine sensors 982 may also include machine setting sensors 991 that sense machine settings. Some examples of machine settings are described above with reference to fig. 1. A front end device (e.g., header) position sensor 993 may sense the position of the header 102, reel 164, cutter 104, or other front end device relative to the frame of the agricultural harvester 100. For example, sensor 993 may sense the height of header 102 above the ground. Machine sensors 982 may also include a front end device (e.g., header) orientation sensor 995. A sensor 995 may sense the orientation of header 102 relative to agricultural harvester 100 or relative to the ground. The machine sensors 982 may include stability sensors 997. The stability sensor 997 senses the oscillating or bouncing motion (and amplitude) of the agricultural harvester 100. Machine sensors 982 may also include a residue setting sensor 999 configured to sense whether agricultural harvester 100 is configured to shred, pile, or otherwise process the residue. Machine sensors 982 may include a cleanroom fan speed sensor 951 that senses the speed of the cleanroom fan 120. The machine sensor 982 may include a recess clearance sensor 953 that senses the clearance between the rotor 112 and the recess 114 on the agricultural harvester 100. The machine sensors 982 may include a chaff screen gap sensor 955 which senses the size of the openings in the chaff screen 122. Machine sensors 982 may include a threshing rotor speed sensor 957 that senses the rotor speed of rotor 112. Machine sensors 982 may include a rotor pressure sensor 959 that senses pressure for driving rotor 112. The machine sensor 982 may include a screen gap sensor 961 that senses the size of the opening in the screen 124. Machine sensors 982 may include a MOG moisture sensor 963 that senses the moisture content of the MOG passing through agricultural harvester 100. Machine sensor 982 may include a machine orientation sensor 965 that senses the orientation of agricultural harvester 100. The machine sensors 982 may include a material feed rate sensor 967 that senses the feed rate of the material as it passes through the feeder housing 106, the clean grain elevator 130, or elsewhere in the agricultural harvester 100. The machine sensor 982 may include a biomass sensor 969 that senses biomass passing through the feeder housing 106, the separator 116, or elsewhere in the agricultural harvester 100. Machine sensors 982 may include a fuel consumption sensor 971 that senses a fuel consumption rate of agricultural harvester 100 over time. Machine sensors 982 may include a power utilization sensor 973 that senses power utilization in agricultural harvester 100, such as which subsystems are utilizing power, or the rate at which subsystems utilize power, or the power distribution between subsystems in agricultural harvester 100. Machine sensors 982 may include tire pressure sensors 977 that sense the inflation pressure in tires 144 of agricultural harvester 100. Machine sensors 982 may include various other machine performance sensors or machine characteristic sensors indicated at block 975. Machine performance sensors and machine characteristic sensors 975 may sense machine performance or characteristics of agricultural harvester 100.

When the agricultural harvester 100 is processing crop material, the harvest material property sensor 984 may sense a characteristic of the severed crop material. Crop properties may include, for example, crop type, crop moisture, grain quality (e.g., cracked grain), MOG content, grain composition (e.g., starch and protein), MOG moisture, and other crop material properties. Other sensors may sense straw "toughness," grain-to-ear adhesion, and other characteristics that may be beneficially used in control treatments to better capture grain, reduce grain damage, reduce power consumption, reduce grain loss, and the like.

The field and soil property sensors 985 may sense characteristics of the field and soil. The field and soil properties may include soil moisture, soil compaction, presence and location of standing water, soil type, and other soil and field characteristics.

The environmental characteristic sensor 987 may sense one or more environmental characteristics. The environmental characteristics may include, for example, wind direction and speed, precipitation, fog, dust levels or other blurs, or other environmental characteristics.

In some examples, one or more sensors shown in fig. 4B are processed to receive processed data 340 and for input to model generator 210. Model generator 210 generates a model indicative of the relationship between the sensor data and one or more previous or predicted information maps. The model is provided to a graph generator 212, and the graph generator 212 generates a graph that maps the predicted sensor data values or associated characteristics corresponding to the sensors from FIG. 4B.

FIG. 5 is a flowchart of an example of the operation of prediction model generator 210 and prediction graph generator 212 in generating predictive machine model 350 and predictive machine characteristics graph 360. At block 362, the prediction model generator 210 and prediction graph generator 212 receive the previous terrain graph 332. At block 364, the processing system 338 receives one or more sensor signals from the machine sensors 336. As described above, the machine sensor 336 may be a power sensor 366, a speed sensor 368, a material distribution sensor 370, or another type of sensor 371.

At block 372, the processing system 338 processes the one or more received sensor signals to generate data indicative of a characteristic of the machine. In some cases, the sensor data may indicate power characteristics, as indicated by block 374. In some cases, as indicated by block 378, the sensor data may be indicative of an agricultural harvester speed. In some cases, as indicated at block 379, the sensor data (e.g., image or images) may be indicative of material distribution within the agricultural harvester. The sensor data may also include other data, as indicated by block 380.

At block 382, the predictive model generator 210 also obtains a geographic location corresponding to the sensor data. For example, predictive model generator 210 may obtain a geographic location from geographic location sensor 204 and determine an accurate geographic location of captured or derived sensor data 340 based on machine delay, machine speed, and the like. Additionally, at block 382, an orientation of the agricultural harvester 100 relative to the terrain feature may be determined. For example, the orientation of the agricultural harvester 100 is obtained because a machine in an inclined position may exhibit different machine characteristics based on its orientation relative to a slope.

At block 384, the predictive model generator 210 generates one or more predictive machine models (e.g., machine model 350) that model a relationship between the terrain characteristics obtained from the prior information map (e.g., prior information map 258) and the machine characteristics or related characteristics sensed by the site sensors 208. For example, predictive model generator 210 may generate a predictive machine model that models a relationship between terrain characteristics and sensed machine characteristics indicated by sensor data 340 obtained from field sensors 208.

At block 386, the predicted machine model (e.g., predicted machine model 350) is provided to the prediction graph generator 212, which the prediction graph generator 212 generates a predicted machine characteristic graph 360 that maps the predicted machine characteristics based on the terrain map and the predicted machine model 350. In some examples, predictive machine characteristics map 360 predicts power characteristics, as indicated by block 387. In some examples, predicted machine characteristics map 360 predicts machine speed, as indicated by block 388. In some examples, predictive machine characteristics map 360 predicts material distribution in the harvester, as indicated by block 389. In some examples, predicted machine characteristics map 360 predicts clutter characteristics such as clutter flow, clutter level, and clutter content, as indicated by block 390. In some examples, predictive machine characteristics map 360 predicts grain loss, as indicated by block 391. In some examples, predictive machine characteristics map 360 predicts grain quality, as indicated by block 392. In other examples, the prediction graph 360 predicts other items or combinations of the above, as indicated by block 393.

Predicted machine characteristic map 360 may be generated during the course of an agricultural operation. Thus, as the agricultural harvester moves through the field to perform an agricultural operation, a predicted machine characteristic map 360 is generated as the agricultural operation is performed.

At block 394, the prediction graph generator 212 outputs the predicted machine characteristic graph 360. At block 391, predicted machine characteristics map generator 212 outputs the predicted machine characteristics map for presentation and possible interaction by operator 260. At block 393, the prediction graph generator 212 may configure the graph for use by the control system 214. At block 395, the prediction graph generator 212 may also provide the graph 360 to the control region generator 213 to facilitate generating the control region. At block 397, the prediction graph generator 212 also configures the graph 360 in other ways. Predicted machine characteristic map 360 (with or without control zones) is provided to control system 214. At block 396, control system 214 generates control signals to control controllable subsystem 216 based on predicted machine characteristics map 360.

It can thus be seen that the present system acquires a prior information map that maps characteristics, such as topographical characteristic information, to different locations in the field. The present system also uses one or more in-situ sensors that sense in-situ sensor data indicative of machine characteristics (e.g., power usage, machine speed, material distribution, grain loss, clutter, or grain quality), and generates a model that models the relationship between the machine characteristics or related characteristics sensed using the in-situ sensors and the characteristics mapped in the prior information map. Thus, the present system uses the model, field data, and prior information maps to generate a function prediction map, and the generated function prediction map may be configured for use by the control system or presented to a local or remote operator or other user. For example, the control system may use the map to control one or more systems of an agricultural harvester.

The discussion refers to processors and servers. In some examples, the processors and servers include computer processors with associated memory and timing circuitry (not separately shown). They are functional parts of the systems or devices to which they belong and which are activated, and facilitate the function of other components or items in those systems.

In addition, several user interface displays are discussed. The display may take a variety of different forms and may have a variety of different user actuatable operator interface mechanisms disposed thereon. For example, the user-actuatable operator interface mechanism may include text boxes, check boxes, icons, links, drop down menus, search boxes, and the like. The user actuatable operator interface mechanism may also be actuated in a variety of different manners. For example, they may be actuated using an operator interface mechanism, such as a pointing device such as a trackball or mouse, hardware buttons, switches, joystick or keyboard, thumb switches or thumb pad, virtual keyboard or other virtual actuators. Additionally, where the screen displaying the user actuatable operator interface mechanism is a touch sensitive screen, the user actuatable operator interface mechanism may be actuated using touch gestures. Additionally, the user-actuatable operator interface mechanism may be actuated using speech commands using speech recognition functionality. Speech recognition may be implemented using speech detection devices, such as microphones, and software for recognizing detected speech and executing commands based on the received speech.

Several data storage devices are also discussed. It will be noted that the data storage devices may each be divided into a plurality of data storage devices. In some examples, one or more data storage devices may be local to the system accessing the data storage device, one or more data storage devices may be all remote from the system utilizing the data storage device, or one or more data storage devices may be local while others are remote. All of these configurations are contemplated herein.

In addition, the figures illustrate several blocks, each having functionality. It will be noted that fewer blocks may be used to illustrate that the functionality attributed to the various blocks is performed by fewer components. In addition, more blocks may be used to illustrate that functionality may be distributed among more components. In different examples, some functionality may be added, and some functionality may be removed.

It will be noted that the above discussion describes a variety of different systems, components, logic, and interactions. It will be understood that any or all of such systems, components, logic, and interactions may be implemented by hardware items, such as processors, memories, or other processing components (some of which are described below), which perform the functions associated with those systems, components, or logic or interactions. Additionally, as described below, any or all of the systems, components, logic, and interactions may be implemented by software loaded into memory and then executed by a processor or server or other computing component. Any or all of the systems, components, logic and interaction may also be implemented by various combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that may be used to implement any or all of the systems, components, logic, and interactions described above. Other configurations may also be used.

Fig. 6 is a block diagram of an agricultural harvester 600, which may be similar to the agricultural harvester 100 shown in fig. 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 the end user to know the physical location or configuration of the system delivering the services. In various examples, the remote server may transmit the service via a wide area network (e.g., the internet) using an appropriate protocol. For example, a remote server may transmit an application via a wide area network and may be accessed through a web browser or any other computing component. The software or components shown in fig. 2 and the data associated therewith may be stored on a server at a remote location. The computing resources in the remote server environment may be consolidated at a remote data center location, or the computing resources may be spread out across multiple remote data centers. The remote server infrastructure may deliver the service through a shared data center even though the service appears to the user to be a single point of access. Accordingly, the components and functionality described herein may be provided from a remote server at a remote location using a remote server architecture. Alternatively, the components and functionality may be provided from a server, or the components and functionality may be installed directly or otherwise on the client device.

In the example shown in fig. 6, some items are similar to those shown in fig. 2, and those items are numbered similarly. Fig. 6 specifically illustrates that either the predictive model generator 210 or the predictive graph generator 212, or both, may be located at a server location 502 remote from the agricultural harvester 600. Thus, in the example shown in fig. 6, agricultural harvester 100 accesses the system through remote server location 502.

Fig. 6 also depicts another example of a remote server architecture. Fig. 6 illustrates that some of the elements of fig. 2 may be located at a remote server location 502, while others may be located elsewhere. As an example, the data store 202 may be disposed 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 are directly accessible by agricultural harvester 600 through a network such as a wide area network or a local area network; the element may be hosted at a remote site through a service; or the elements may be provided as a service or accessed through a connected service residing at a remote location. In addition, the data may be stored anywhere, and the stored data may be asked by or forwarded to an operator, user, or system. For example, a physical carrier may be used instead of or in addition to an electromagnetic wave carrier. In some examples where wireless telecommunication services are poorly covered or not present, another machine, such as a fuelling vehicle or other mobile machine or vehicle, may have an automated, semi-automated, or manual information collection system. When the combine 600 is near a machine (e.g., a fuelling vehicle) containing an information collection system prior to fuelling, the information collection system collects 600 information from the combine using any type of temporary dedicated wireless connection. The collected information may then be forwarded to another network when the machine containing the received information arrives at a location where wireless telecommunication service coverage or other wireless coverage is available. For example, a refueling truck may enter an area with wireless communication coverage while traveling to a location for refueling other machines or while in a main fuel storage location. All of these architectures are contemplated herein. Further, the information may be stored on the agricultural harvester 600 until the agricultural harvester 600 enters an area with wireless communication coverage. The agricultural harvester 600 itself can send the information to another network.

It will also be noted that the elements of fig. 2, or portions thereof, may be provided on a variety of different devices. One or more of those devices may include an on-board computer, electronic control unit, display unit, server, desktop computer, laptop computer, tablet computer, or other mobile device (e.g., palmtop computer, cellular telephone, smart phone, multimedia player, personal digital assistant, etc.).

In some examples, the remote server architecture 500 may include network security measures. Without limitation, these measures may include encryption of data on storage devices, encryption of data sent between network nodes, authentication of a person or process accessing the data, and the use of a ledger to record metadata, data transfers, data access, and data transformations. In some examples, the ledger can be distributed and immutable (e.g., implemented as a blockchain).

FIG. 7 is a simplified block diagram of one illustrative example of a handheld or mobile computing device that may be used as a user's or customer's handheld device 16 in which the present system (or portions thereof) may be deployed. For example, the mobile device may be deployed in an operator compartment of the combine harvester 100 for generating, processing, or displaying the above-described figures. Fig. 8-9 are examples of handheld or mobile devices.

Fig. 7 provides a general block diagram of components of client device 16 that may run, interact with, or both some of the components shown in fig. 2. In device 16, a communication link 13 is provided that allows the handheld device to communicate with other computing devices and, in some examples, provide a channel for receiving information automatically (e.g., by scanning). Examples of communication links 13 include those that allow communication via one or more communication protocols (e.g., wireless services for providing cellular access to a network) as well as protocols that provide local wireless connectivity with a network.

In other examples, the application may be received on a removable Secure Digital (SD) card connected to interface 15. The interface 15 and communication link 13 communicate with a processor 17 (which may also embody processors or servers from other figures) along a bus 19, the bus 19 also being connected to a memory 21 and input/output (I/O) components 23 as well as a clock 25 and a position system 27.

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

Clock 25 illustratively includes a real-time clock component that outputs a time and date. Illustratively, it may also provide timing functionality for the processor 17.

The location system 27 illustratively includes components that output the current geographic location of the device 16. This may include, for example, a Global Positioning System (GPS) receiver, LORAN system, dead reckoning system, cellular triangulation system, or other positioning system. For example, the location system 27 may also include mapping software or navigation software that generates desired maps, navigation routes, and other geographic functions.

The memory 21 stores an operating system 29, network settings 31, applications 33, application configuration settings 35, data storage 37, communication drivers 39, and communication configuration settings 41. The memory 21 may comprise all types of tangible volatile and non-volatile computer-readable memory devices. Memory 21 may also include computer storage media (described below). The memory 21 stores computer readable instructions which, when executed by the processor 17, cause the processor to perform computer implemented steps or functions in accordance with the instructions. The processor 17 may also be activated by other components to facilitate its functions.

Fig. 8 illustrates an example where the device 16 is a tablet computer 600. In fig. 8, a computer 600 is shown having a user interface display screen 602. The screen 602 may be a touch screen or a pen-enabled interface that receives input from a pen or stylus. Tablet computer 600 may also use an on-screen virtual keyboard. Of course, the computer 600 may also be attached to a keyboard or other user input device by a suitable attachment mechanism (e.g., a wireless link or a USB port), for example. The computer 600 may also illustratively receive speech input.

Fig. 9 is similar to fig. 8 except that the device is a smartphone 71. The smartphone 71 has a touch sensitive display 73 that displays icons or tiles or other user input mechanisms 75. The user may use the mechanism 75 to run applications, make phone calls, perform data transfer operations, and the like. Typically, the smart phone 71 builds on a mobile operating system and provides a higher level of computing power and connectivity than a feature phone.

It is noted that other forms of device 16 are possible.

FIG. 10 is an example of a computing environment in which the elements of FIG. 2 may be deployed. With reference to FIG. 10, an example system for implementing some embodiments includes a computing device in the form of a computer 810 programmed to operate as described above. Components of computer 810 may include, but are not limited to, a processing unit 820 (including a processor or server from the previous figures), a system memory 830, and a system bus 821 that couples various system components including the system memory to the processing unit 820. The system bus 821 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The memory and programs described with reference to fig. 2 may be deployed in corresponding portions of fig. 10.

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 may comprise computer storage media and communication media. The computer storage medium is distinct from and does not include a modulated data signal or a carrier wave. Computer-readable media 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 may embody computer readable instructions, data structures, program modules, or other data in a transport mechanism and include 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.

The system memory 830 includes computer storage media in the form of volatile and/or nonvolatile memory such as Read Only Memory (ROM)831 and Random Access Memory (RAM)832, or both. A basic input/output system 833(BIOS), containing the basic routines that help to transfer information between elements within computer 810, such as during start-up, is typically stored in ROM 831. RAM 832 typically contains data or program modules or both that are immediately accessible to and/or presently being operated on by processing unit 820. By way of example, and not limitation, fig. 10 illustrates operating system 834, application programs 835, other program modules 836, and program data 837.

The computer 810 may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only, FIG. 10 illustrates a hard disk drive 841 that reads from or writes to non-removable, nonvolatile magnetic media, an optical disk drive 855, and a nonvolatile optical disk 856. The hard disk drive 841 is typically connected to the system bus 821 through a non-removable memory interface such as interface 840, and optical disk drive 855 is typically connected to the system bus 821 by a removable memory interface, such as interface 850.

Alternatively or in addition, the functions described herein may be performed, at least in part, by one or more hardware logic components. By way of example, and not limitation, exemplary 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 Systems (SOCs), Complex Programmable Logic Devices (CPLDs), and so forth.

The drives and their associated computer storage media discussed above and illustrated in FIG. 10, provide storage of computer readable instructions, data structures, program modules and other data for the computer 810. In fig. 10, for example, hard disk drive 841 is illustrated as storing operating system 844, application programs 845, other program modules 846, and program data 847. Note that these components can either be the same as or different from operating system 834, application programs 835, other program modules 836, and program data 837.

A user may enter commands and information into the computer 810 through input devices such as a keyboard 862, a microphone 863, and a pointing device 861 (e.g., a mouse, trackball or touch pad). Other input devices (not shown) may 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, but may be connected by other interface and bus structures. A visual display 891 or other type of display device is also connected to the system bus 821 via an interface, such as a video interface 890. In addition to the monitor, computers may also include other peripheral output devices such as speakers 897 and printer 896, which may be connected through an output peripheral interface 895.

The computer 810 operates in a networked environment using logical connections (e.g., a controller area network, CAN, an area network, LAN, or wide area network, WAN) to one or more remote computers (e.g., a remote computer 880).

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 (e.g., the Internet). In a networked environment, program modules may be stored in the remote memory storage device. For example, FIG. 10 illustrates remote application programs 885 as residing on remote computer 880.

It should also be noted that the different examples described herein may be combined in different ways. That is, portions of one or more examples may be combined with portions of one or more other examples. All of which are contemplated herein.

Example 1 is an agricultural work machine, comprising:

a communication system receiving a terrain map mapping terrain values of a terrain characteristic to different geographic locations in a field;

a geographic position sensor that detects a geographic position of the agricultural work machine;

a field sensor that detects a value of a machine characteristic corresponding to a geographical location;

a predictive model generator that generates a predictive agricultural model that models a relationship between a terrain characteristic and a machine characteristic based on one of a terrain value of the terrain characteristic corresponding to the geographic location in the previous information map and a value of the machine characteristic sensed by the field sensor at the geographic location; and

a prediction map generator that generates a functional prediction agricultural map of the field that maps predicted values of the machine characteristics to different geographic locations in the field based on the terrain values in the previous information map and based on the prediction agricultural model.

Example 2 is the agricultural work machine of any or all of the previous examples, wherein the predictive graph generator is to configure the functionally predictive agricultural graph for use by a control system that is to generate control signals to control controllable subsystems on the agricultural work machine based on the functionally predictive agricultural graph.

Example 3 is the agricultural work machine of any or all of the previous examples, wherein the field sensors on the agricultural work machine are configured to detect an internal material distribution corresponding to a geographic location as a value of the machine characteristic.

Example 4 is the agricultural work machine of any or all of the previous examples, wherein the field sensor on the agricultural work machine is configured to detect grain loss or grain quality corresponding to the geographic location as a value of the machine characteristic.

Example 5 is the agricultural work machine of any or all of the previous examples, wherein the field sensor on the agricultural work machine is configured to detect a nuisance characteristic corresponding to the geographic location as a value of the machine characteristic.

Example 6 is the agricultural work machine of any or all of the previous examples, wherein the field sensor on the agricultural work machine is configured to detect a power characteristic corresponding to the geographic location as a value of the machine characteristic.

Example 7 is the agricultural work machine of any or all of the previous examples, wherein the field sensors include one or more of: voltage sensors, current sensors, torque sensors, hydraulic flow sensors, force sensors, bearing load sensors, and rotation sensors.

Example 8 is the agricultural work machine of any or all of the previous examples, wherein the power characteristic comprises a power usage of one or more subsystems of the agricultural work machine.

Example 9 is the agricultural work machine of any or all of the previous examples, wherein the field sensor on the agricultural work machine is configured to detect a machine speed corresponding to the geographic location as a value of the machine characteristic.

Example 10 is the agricultural work machine of any or all of the previous examples, wherein the field sensor comprises a geographic position sensor.

Example 11 is the agricultural work machine of any or all of the previous examples, wherein the machine speed comprises a machine direction.

Example 12 is a computer-implemented method of generating a functional predictive agricultural map, comprising:

receiving, at an agricultural work machine, a prior information map indicating terrain characteristic values corresponding to different geographic locations in a field as a first agricultural characteristic;

detecting a geographic location of the agricultural work machine;

detecting a machine characteristic as a second agricultural characteristic corresponding to the geographic location using the site sensor;

generating a predictive agricultural model that models a relationship between the first agricultural characteristic and the second agricultural characteristic; and

the control prediction map generator generates a functional prediction agricultural map of the field mapping predicted values of the second agricultural property to different locations in the field based on the values of the first agricultural property in the previous information map and the predicted agricultural model.

Example 13 is the computer-implemented method of any or all of the previous examples, and further comprising configuring the function-predictive agricultural map to facilitate the control system generating control signals to control controllable subsystems on the agricultural work machine based on the function-predictive agricultural map.

Example 14 is the computer-implemented method of any or all of the previous examples, wherein the terrain characteristic value comprises a value indicative of one or more of a ground height, a ground grade, and a ground roughness.

Example 15 is the computer-implemented method of any or all of the previous examples, wherein detecting the machine characteristic comprises detecting a power usage of a controllable subsystem of the agricultural work machine as the machine characteristic.

Example 16 is the computer-implemented method of any or all of the previous examples, wherein detecting the machine characteristic comprises detecting an internal material distribution.

Example 17 is the computer-implemented method of any or all of the previous examples, wherein detecting the machine characteristic comprises detecting a nuisance characteristic, grain loss, or grain quality.

Example 18 is the computer-implemented method of any or all of the previous examples, further comprising controlling the operator interface mechanism to present the predicted agricultural map.

Example 19 is an agricultural work machine, comprising:

a communication system that receives a terrain map indicating terrain values corresponding to different geographic locations in a field;

a field sensor that detects a machine characteristic value of a machine characteristic corresponding to the geographical position;

a predictive model generator that generates a predictive machine model that models a relationship between a terrain value and a machine characteristic based on the terrain value at a geographic location in the terrain map and the machine characteristic value of the machine characteristic sensed by the in-situ sensor at the geographic location; and

a prediction map generator that generates a functional predicted machine characteristic map of the field that maps predicted machine characteristic values to different locations in the field based on the terrain values in the previous terrain map and based on the predicted machine model.

Example 20 is the agricultural work machine of any or all of the previous examples, wherein the field sensor detects one or more of: power characteristics, machine speed, internal material distribution, grain loss, impurity characteristics, or grain quality.

Although 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 the claims.

Claims

1. An agricultural work machine (100), comprising:

a communication system (206) that receives a terrain map that maps terrain values of a terrain characteristic to different geographic locations in a field;

a geographic position sensor (204) that detects a geographic position of the agricultural work machine;

a field sensor (208) that detects a value of a machine characteristic corresponding to a geographic location;

a predictive model generator (210) that generates a predictive agricultural model that models a relationship between terrain characteristics and machine characteristics based on one of terrain values for the terrain characteristics corresponding to a geographic location in a previous information map and values for the machine characteristics sensed by a field sensor at the geographic location; and

a prediction map generator (263) that generates a functional prediction agricultural map of the field that maps predicted values of the machine characteristics to different geographic locations in the field based on the terrain values in the previous information map and based on the prediction agricultural model.

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

3. The agricultural work machine of claim 1, wherein the in-situ sensor on the agricultural work machine is configured to detect an internal material distribution corresponding to a geographic location as a value of a machine characteristic.

4. The agricultural work machine of claim 1, wherein the field sensor on the agricultural work machine is configured to detect grain loss or grain quality corresponding to a geographic location as a value of the machine characteristic.

5. The agricultural work machine of claim 1, wherein the field sensor on the agricultural work machine is configured to detect a miscellaneous characteristic corresponding to the geographic location as a value of the machine characteristic.

6. The agricultural work machine of claim 1, wherein the field sensor on the agricultural work machine is configured to detect a power characteristic corresponding to the geographic location as a value of the machine characteristic.

7. The agricultural work machine of claim 6, wherein the field sensor comprises one or more of: voltage sensors, current sensors, torque sensors, hydraulic pressure sensors, hydraulic flow sensors, force sensors, bearing load sensors, and rotation sensors.

8. The agricultural work machine of claim 6, wherein the power characteristic comprises power usage of one or more subsystems of the agricultural work machine.

9. A computer-implemented method of generating a functional predictive agricultural map, comprising:

receiving, at an agricultural work machine (100), a previous information map (258) indicating terrain characteristic values corresponding to different geographical locations in a field as a first agricultural characteristic;

detecting a geographical position of an agricultural work machine (100);

detecting the machine characteristic with the presence sensor (208) as a second agricultural characteristic corresponding to the geographic location;

a control prediction map generator (212) generates a functional prediction agricultural map of the field that maps predicted values of the second agricultural property to different locations in the field based on the predicted agricultural model and the values of the first agricultural property in the previous information map.

10. An agricultural work machine (100), comprising:

a communication system (206) that receives a terrain map indicative of terrain values corresponding to different geographic locations in a field;

a field sensor (208) that detects a machine characteristic value of a machine characteristic corresponding to the geographical position;

a predictive model generator (210) that generates a predictive machine model that models a relationship between a terrain value and a machine characteristic based on the terrain value at a geographic location in the terrain map and the machine characteristic value of the machine characteristic sensed by the field sensor at the geographic location; and

a prediction map generator (212) generates a functional predicted machine characteristic map of the field mapping the predicted machine characteristic values to different locations in the field based on the terrain values in the previous terrain map and based on the predicted machine model.