# US 12,216,472 B2 · Map generation and control system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,216,472 B2 - Document ID: US12216472B2 - Status: Granted - Kind: B2 - Application number: 18/447,725 - Filed: 2023-08-10 - Published: 2023-12-07 - Granted: 2025-02-04 - Earliest claimed: 2020-10-09 - Assignees: Deere & Company - Applicants: Deere & Company - Inventors: Nathan R. Vandike; Bhanu Kiran Reddy Palla; Federico Pardina-Malbran; Matthew T. Wold; Cody W. Best; Noel W. Anderson - Web page: https://patentgrep.com/patent/US12216472B2 ## CPC - G05D 1/0212: {with means for defining a desired trajectory (involving a plurality of land vehicles G05D1/0287)} - G05D 1/0274: {using mapping information stored in a memory device (navigation using map-matching G01C21/30)} - A01D 41/127: Control or measuring arrangements specially adapted for combines - B60K 35/10: Input arrangements, i.e. from user to vehicle, associated with vehicle functions or specially adapted therefor - B60K 35/22: Display screens - B60K 35/26: using acoustic output - B60K 35/28: characterised by the type of the output information, e.g. video entertainment or vehicle dynamics information; characterised by the purpose of the output information, e.g. for attracting the attention of the driver - B60K 35/80: Arrangements for controlling instruments - B60K 35/85: Arrangements for transferring vehicle- or driver-related data - G01C 21/3807: {characterised by the type of data} - G01C 21/3826: {Terrain data} - G01C 21/3848: {Data obtained from both position sensors and additional sensors} - G05D 1/0221: {involving a learning process} - G05D 1/646: Following a predefined trajectory, e.g. a line marked on the floor or a flight path - G06N 3/08: Learning methods - B60K 2360/166: Navigation - G01N 33/0098: {Plants or trees (wood G01N33/46)} - G01N 33/246: {for water content} - G06N 20/00: Machine learning - G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES - A01D: HARVESTING; MOWING - B60K: ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) ## Abstract One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control. ## Claims ### Claim 1 An agricultural work machine comprising; a controllable subsystem; a communication system configured to obtain an information map that includes yield values corresponding to different geographic locations in a field; an in-situ sensor configured detects a value of an agricultural characteristic corresponding to a geographic location; one or more processors; memory; and computer executable instructions, stored in the memory, that, when executed by the one or more processors, configure the one or more processors to: generate a predictive agricultural model that models a relationship between the agricultural characteristic and yield based on the value of the agricultural characteristic, detected by the in-situ sensor, corresponding to the geographic location and a yield value in the information map corresponding to the geographic location; and control the controllable subsystem based on the predictive agricultural model. ### Claim 2 (depends on 1) The agricultural work machine of claim 1, wherein the computer executable instructions, when executed by the one or more processors, further configure the one or more processors to: generate a functional predictive agricultural map of the field that maps predictive values of the agricultural characteristic to the different geographic locations in the field based on the yield value in the information map and based on the predictive agricultural model; and control the controllable subsystem based on the functional predictive agricultural map. ### Claim 3 (depends on 1) The agricultural work machine of claim 1, wherein the information map is derived from vegetative index data of the field. ### Claim 4 (depends on 1) The agricultural work machine of claim 1, wherein the in-situ sensor is configured to detect, as the value of the agricultural characteristic, a biomass value corresponding to the geographic location and wherein the predictive agricultural model models, as the relationship, a relationship between biomass and yield based on the value of biomass, detected by the in-situ sensor, corresponding to the geographic location and the yield value in the information map corresponding to the geographic location. ### Claim 5 (depends on 1) The agricultural work machine of claim 1, wherein the in-situ sensor is configured to detect, as the value of the agricultural characteristic, an operator input, indicative of an operator commanded setting of the agricultural work machine, corresponding to the geographic location and wherein the predictive agricultural model models, as the relationship, a relationship between operator input and yield based on the operator input, detected by the in situ sensor, corresponding to the geographic location and the yield value in the information map corresponding to the geographic location. ### Claim 6 (depends on 1) The agricultural work machine of claim 1, wherein the information map comprises a historical yield map that includes, as values of yield, historical values of yield corresponding to the different geographic locations in the field. ### Claim 7 (depends on 6) The agricultural work machine of claim 6, wherein the in-situ sensor is configured to detect, as the value of the agricultural characteristic, an in-situ yield value corresponding to the geographic location and wherein the predictive agricultural model models, as the relationship, a relationship between in-situ yield and historical yield based on the in-situ yield value, detected by the in-situ sensor, corresponding to the geographic location and a historical yield value in the information map corresponding to the geographic location. ### Claim 8 (depends on 1) The agricultural work machine of claim 1, wherein the controllable subsystem is controlled to control a setting of the of the agricultural work machine. ### Claim 9 (depends on 8) The agricultural work machine of claim 8, wherein the setting comprises one of: a rotor speed setting; a thresher clearance setting; a header position setting; a cleaning fan speed setting; an agricultural work machine speed setting; a sieve setting; or a chaffer setting. ### Claim 10 A computer implemented method of controlling an agricultural work machine comprising: obtaining an information map that includes yield values corresponding to different geographic locations in a field; detecting, with an in-situ sensor, a value of an agricultural characteristic corresponding to a geographic location; generating a predictive agricultural model that models a relationship between the agricultural characteristic and yield based on the value of the agricultural characteristic, detected by the in-situ sensor, corresponding to the geographic location and a yield value, in the information map, corresponding to the geographic location; and controlling a controllable subsystem of the agricultural work machine based on the predictive agricultural model. ### Claim 11 (depends on 10) The computer implemented method of claim 10 and further comprising generating a functional predictive agricultural map of the field that maps predictive values of the agricultural characteristic to the different geographic locations in the field based on the yield values in the information map and based on the predictive agricultural model, wherein controlling the controllable subsystem comprises controlling the controllable subsystem based on the functional predictive agricultural map. ### Claim 12 (depends on 11) The computer implemented method of claim 11, wherein detecting, with the in-situ sensor, the value of the agricultural characteristic corresponding to the geographic location comprises detecting, with the in-situ sensor, as the value of the agricultural characteristic, a biomass value corresponding to the geographic location, and wherein generating the predictive agricultural model comprises, generating the predictive agricultural model modeling, as the relationship, a relationship between biomass and yield based on the biomass value, detected by the in-situ sensor, corresponding to the geographic location and the yield value, in the information map, corresponding to the geographic location and as being configured to receive an input yield value as a model input and to generate a predictive biomass value as a model output based on the relationship. ### Claim 13 (depends on 11) The computer implemented method of claim 11, wherein detecting, with the in-situ sensor, the value of the agricultural characteristic corresponding to the geographic location comprises detecting, with the in-situ sensor, as the value of the agricultural characteristic, an operator input, indicative of an operator commanded setting of the agricultural work machine, corresponding to the geographic location, and wherein generating the predictive agricultural model comprises, generating the predictive agricultural model modeling, as the relationship, a relationship between operator input and yield based on the operator input, detected by the in-situ sensor, corresponding to the geographic location and the yield value, in the information map, corresponding to the geographic location and as being configured to receive an input yield value as a model input and to generate a predictive operator input as a model output based on the relationship. ### Claim 14 (depends on 11) The computer implemented method of claim 11, wherein obtaining the information map that includes yield values corresponding to different geographic locations in the field, comprises obtaining, as the information map, a historical yield map that includes historical yield values corresponding to different geographic locations in the field, wherein detecting, with the in-situ sensor, the value of the agricultural characteristic corresponding to the geographic location comprises detecting, with the in-situ sensor, as the value of the agricultural characteristic, an in-situ yield value corresponding to the geographic location, and wherein generating the predictive agricultural model comprises, generating the predictive agricultural model modeling, as the relationship, a relationship between in-situ yield and historical yield based on the in-situ yield, detected by the in-situ sensor, corresponding to the geographic location and a historical yield value, in the historical yield map, corresponding to the geographic location and as being configured to receive an input historical yield value as a model input and to generate a predictive in-situ yield value as a model output based on the relationship. ### Claim 15 (depends on 10) The computer implemented method of claim 10, wherein controlling the controllable subsystem comprises: generating a setting control signal based on the predictive agricultural model; and controlling the controllable subsystem based on the setting control signal to control a setting of the agricultural work machine. ### Claim 16 (depends on 10) The computer implemented method of claim 10, wherein controlling the controllable subsystem comprises: generating a speed control signal based on the predictive agricultural model; and controlling the controllable subsystem based on the speed control signal to control a speed of the agricultural work machine. ### Claim 17 (depends on 10) The computer implemented method of claim 10, wherein controlling the controllable subsystem comprises: generating a feed rate control signal based on the predictive agricultural model; and controlling the controllable subsystem based on the feed rate control signal to control feed rate of material through the agricultural work machine. ### Claim 18 An agricultural system comprising: a communication system configured to receive an information map that includes yield values corresponding to different geographic locations in a field; an in-situ sensor configured to detect a value of an agricultural characteristic corresponding to a geographic location; one or more processors; memory; and computer executable instructions, stored in the memory, that, when executed by the one or more processors, configure the one or more processors to: generate a predictive agricultural model that models a relationship between the agricultural characteristic and yield based on the value of the agricultural characteristic, detected by the in-situ sensor, corresponding to the geographic location and a yield value in the information map corresponding to the geographic location; and control a controllable subsystem on an agricultural work machine based on the predictive agricultural model. ### Claim 19 (depends on 18) The agricultural system of claim 18, wherein the computer executable instructions, when executed by the one or more processors, further configure the one or more processors to: generate a functional predictive agricultural map of the field that maps predictive values of the agricultural characteristic to the different geographic locations in the field based on the yield values in the information map and based on the predictive agricultural model; and control the controllable subsystem on the agricultural work machine based on the functional predictive agricultural map. ### Claim 20 (depends on 18) The agricultural system of claim 18, wherein the controllable subsystem is controlled based on the predictive agricultural model to controllably actuate one of: ground engaging components of the agricultural work machine; a chopper of the agricultural work machine; a spreader of the agricultural work machine; a threshing rotor of the agricultural work machine; a sieve of the agricultural work machine; a chaffer of the agricultural work machine; concaves of the agricultural work machine; a discharge beater of the agricultural work machine; a tailings elevator of the agricultural work machine; a grain elevator of the agricultural work machine; a header of the agricultural work machine; a component of the header of the agricultural work machine; or a cleaning fan of the agricultural work machine.