# US 12,178,156 B2 · Predictive map generation and control > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,178,156 B2 - Document ID: US12178156B2 - Status: Granted - Kind: B2 - Application number: 18/184,984 - Filed: 2023-03-16 - Published: 2023-07-13 - Granted: 2024-12-31 - Earliest claimed: 2018-10-26 - Assignees: Deere & Company - Applicants: Deere & Company - Inventors: Nathan R. Vandike; Bhanu Kiran Reddy Palla; Noel W. Anderson - Web page: https://patentgrep.com/patent/US12178156B2 ## CPC - A01D 41/127: Control or measuring arrangements specially adapted for combines - A01F 15/0715: {Wrapping the bale in the press chamber before opening said chamber} - 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} - G01N 21/25: Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands - A01B 79/005: {Precision agriculture} - G01N 21/31: Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry {(G01N21/72 takes precedence)} - G01N 33/0098: {Plants or trees (wood G01N33/46)} - G01N 2021/8466: {Investigation of vegetal material, e.g. leaves, plants, fruits} - G06N 20/00: Machine learning - A01D: HARVESTING; MOWING - A01F: PROCESSING OF HARVESTED PRODUCE; HAY OR STRAW PRESSES; DEVICES FOR STORING AGRICULTURAL OR HORTICULTURAL PRODUCE (devices for topping or skinning onions or flower bulbs A23N15/08) - 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) - 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) - A01B: SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES OR IMPLEMENTS, IN GENERAL (making or covering furrows or holes for sowing, planting or manuring A01C5/00; mowers convertible to soil working apparatus or capable of soil working A01D42/04; mowers combined with soil working implements A01D43/12; soil working for engineering purposes E01, E02, E21) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## 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 communication system that receives an information map that includes values of an optical characteristic corresponding to different geographic locations in a field; a geographic position sensor that detects a geographic location of the agricultural work machine; an in-situ sensor that detects a value of an agricultural characteristic corresponding to the geographic location; a predictive model generator that generates a predictive characteristic model that models a relationship between the optical characteristic and the agricultural characteristic based on a value of the optical characteristic in the information map at the geographic location and the value of the agricultural characteristic detected by the in-situ sensor corresponding to the geographic location; and a predictive map generator that generates a functional predictive map of the field, that maps predictive values of the agricultural characteristic to the different geographic locations in the field, based on the values of the optical characteristic in the information map and based on the predictive characteristic model. ### Claim 2 (depends on 1) The agricultural work machine of claim 1, wherein the predictive map generator configures the functional predictive map for consumption by a control system that generates control signals to control a controllable subsystem on the agricultural work machine based on the functional predictive map. ### Claim 3 (depends on 1) The agricultural work machine of claim 1, wherein the in-situ sensor on the agricultural work machine is configured to detect, as the value of the agricultural characteristic, a non-machine characteristic corresponding to the geographic location. ### Claim 4 (depends on 1) The agricultural work machine of claim 1, wherein the in-situ sensor on the agricultural work machine is configured to detect, as the value of the agricultural characteristic, a machine characteristic corresponding to the geographic location. ### Claim 5 (depends on 1) The agricultural work machine of claim 1, wherein the information map comprises an optical characteristic map that maps, as the optical characteristic, vegetative index values to the different geographic locations in the field, and wherein the predictive model generator is configured to identify a relationship between the vegetative index values and the agricultural characteristic based on the value of the agricultural characteristic detected at the geographic location and a vegetative index value, in the optical characteristic map, at the geographic location, the predictive characteristic model being configured to receive an input vegetative index value as a model input and generate a predicted agricultural characteristic value as a model output based on the identified relationship. ### Claim 6 (depends on 1) The agricultural work machine of claim 1, wherein the information map comprises an optical characteristic map that maps, as the optical characteristic, texture values to the different geographic locations in the field, and wherein the predictive model generator is configured to identify a relationship between the texture values and the agricultural characteristic based on the value of the agricultural characteristic detected at the geographic location and a texture value, in the optical characteristic map, at the geographic location, the predictive characteristic model being configured to receive an input texture value as a model input and generate a predicted agricultural characteristic value as a model output based on the identified relationship. ### Claim 7 (depends on 6) The agricultural work machine of claim 6, wherein at least some of the texture values are indicative of standing, downed or partially downed crop. ### Claim 8 (depends on 7) The agricultural work machine of claim 7, wherein at least some of the texture values are indicative of a downed crop orientation. ### Claim 9 (depends on 6) The agricultural work machine of claim 6, wherein at least some of the texture values are indicative of a species of plant. ### Claim 10 (depends on 1) The agricultural work machine of claim 1, wherein the information map comprises an optical characteristic map that maps, as the optical characteristic, wavelength values to the different geographic locations in the field, and wherein the predictive model generator is configured to identify a relationship between wavelength and the agricultural characteristic based on the value of the agricultural characteristic detected at the geographic location and a wavelength value, in the optical characteristic map, at the geographic location, the predictive characteristic model being configured to receive an input wavelength value as a model input and generate a predicted characteristic value as a model output based on the identified relationship. ### Claim 11 (depends on 1) The agricultural work machine of claim 1, further comprising an operator interface mechanism that displays the functional predictive map. ### Claim 12 A computer implemented method of generating a functional predictive map, the computer implemented method comprising: receiving an information map, at an agricultural work machine, that indicates values of an optical characteristic corresponding to different geographic locations in a field; detecting a geographic location of the agricultural work machine; detecting, with an in-situ sensor, a value of an agricultural characteristic corresponding to the geographic location; generating a predictive characteristic model that models a relationship between the optical characteristic and the agricultural characteristic; and controlling a predictive map generator to generate the functional predictive map of the field, that maps predictive values of the agricultural characteristic to the different locations in the field based on the optical characteristic values in the information map and the predictive characteristic model. ### Claim 13 (depends on 12) The computer implemented method of claim 12 and further comprising: configuring the functional predictive map for a control system that generates control signals to control a controllable subsystem on the agricultural work machine based on the functional predictive map. ### Claim 14 (depends on 12) The computer implemented method of claim 12, wherein detecting, with the in-situ sensor, the value of the agricultural characteristic comprises detecting a non-machine characteristic corresponding to the geographic location. ### Claim 15 (depends on 14) The computer implemented method of claim 14, wherein generating the predictive characteristic model comprises: identifying a relationship between the optical characteristic values and the non-machine characteristic based on the non-machine characteristic detected at the geographic location and the optical characteristic value, in the information map, at the geographic location; and controlling a predictive model generator to generate the predictive characteristic model that receives an input optical characteristic value as a model input and generates a non-machine characteristic value as a model output based on the identified relationship. ### Claim 16 (depends on 12) The computer implemented method of claim 12, wherein detecting, with the in-situ sensor, the value of the agricultural characteristic comprises detecting a machine characteristic corresponding to the geographic location. ### Claim 17 (depends on 16) The computer implemented method of claim 16, wherein generating the predictive characteristic model comprises: identifying a relationship between the optical characteristic values and the machine characteristic based on the machine characteristic detected at the geographic location and the optical characteristic value, in the information map, at the geographic location; and controlling a predictive model generator to generate the predictive characteristic model that receives an input optical characteristic value as a model input and generates a machine characteristic value as a model output based on the identified relationship. ### Claim 18 (depends on 12) The computer implemented method of claim 12, further comprising: controlling an operator interface mechanism to present the functional predictive map. ### Claim 19 An agricultural system comprising: a communication system that receives an information map that includes values of an optical characteristic corresponding to different geographic locations in a field; a geographic position sensor that detects a geographic location of an agricultural work machine; an in-situ sensor that detects a value of an agricultural characteristic corresponding to the geographic location; a predictive model generator that generates a predictive characteristic model that models a relationship between the optical characteristic and the agricultural characteristic based on a value of the optical characteristic in the information map at the geographic location and the value of the agricultural characteristic detected by the in-situ sensor corresponding to the geographic location; and a predictive map generator that generates a functional predictive map of the field, that maps predictive values of the agricultural characteristic to the different geographic locations in the field, based on the values of the optical characteristic in the information map and based on the predictive characteristic model. ### Claim 20 (depends on 19) The agricultural system of claim 19, wherein the values of the optical characteristic comprise wavelength values.