# US 12,178,157 B2 · Crop state map generation and control system

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- Number: US 12,178,157 B2
- Document ID: US12178157B2
- Status: Granted
- Kind: B2
- Application number: 18/446,230
- Filed: 2023-08-08
- Published: 2023-12-14
- Granted: 2024-12-31
- Earliest claimed: 2020-10-09
- Assignees: Deere & Company
- Applicants: Deere & Company
- Inventors: Nathan R. Vandike; Bhanu Kiran Reddy Palla; Noel W. Anderson; Federico Pardina-Malbran
- Web page: https://patentgrep.com/patent/US12178157B2

## CPC

- A01D 41/127: Control or measuring arrangements specially adapted for combines
- G01B 11/0608: {Height gauges}
- G01N 33/0098: {Plants or trees (wood G01N33/46)}
- G01S 19/01: Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G06N 20/00: Machine learning
- A01B 71/02: Setting or adjusting mechanisms
- A01D 41/1278: {for automatic steering}
- A01D 41/141: {Automatic header control}
- G01B 11/022: {by means of tv-camera scanning}
- G01S 13/88: Radar or analogous systems specially adapted for specific applications (electromagnetic prospecting or detecting of objects, e.g. near-field detection, G01V3/00)
- G01S 15/88: Sonar systems specially adapted for specific applications (seismic or acoustic prospecting or detecting G01V1/00)
- G01S 17/88: Lidar systems specially adapted for specific applications
- A01D: HARVESTING; MOWING
- G01B: MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- 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)
- G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- 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)

## 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 configured to obtain an information map that includes values of a first agricultural characteristic corresponding to different geographic locations in a field;
a geographic position sensor configured to detect a geographic location of the agricultural work machine;
an in-situ sensor configured to detect, as a value of a second agricultural characteristic, a crop state value corresponding to the geographic location; and
a predictive model generator configured to generate a predictive agricultural model that models a relationship between the first agricultural characteristic and the second agricultural characteristic based on a value of the first agricultural characteristic in the information map corresponding to the geographic location and the detected value of the second agricultural characteristic corresponding to the geographic location.

### Claim 2 (depends on 1)

The agricultural work machine of claim 1, a control system configured to generate a control signal to control a controllable subsystem on the agricultural work machine based on the predictive agricultural model.

### Claim 3 (depends on 1)

The agricultural work machine of claim 1, wherein the crop state value indicates at least one of a magnitude or a direction of leaning of a crop to which the crop state value corresponds.

### Claim 4 (depends on 1)

The agricultural work machine of claim 1, wherein the information map comprises a vegetative index map that includes, as the values of the first agricultural characteristic, vegetative index values corresponding to the different geographic locations in the field, and wherein the predictive model generator is configured to model, as the relationship, a relationship between crop state and vegetative index based on the detected crop state value corresponding to the geographic location and a vegetative index value in the vegetative index map corresponding to the geographic location, the predictive agricultural model being configured to receive an input vegetative index value as a model input and generate a predictive crop state value as a model output based on the modeled relationship.

### Claim 5 (depends on 1)

The agricultural work machine of claim 1, wherein the information map comprises a seeding characteristic map that includes, as values of the first agricultural characteristic, values of a seeding characteristic corresponding to the different geographic locations in the field and wherein the predictive agricultural model models, as the relationship, a relationship between crop state and the seeding characteristic based on the detected crop state value corresponding to the geographic location and a value of the seeding characteristic in the seeding characteristic map corresponding to the geographic location, the predictive agricultural model being configured to receive an input value of the seeding characteristic as a model input and generate a predictive crop state value as a model output based on the modeled relationship.

### Claim 6 (depends on 5)

The agricultural work machine of claim 5, wherein the seeding characteristic comprises one of:
genetic stalk or stem strength;
genetic susceptibility to lodging; or
seed planting density.

### Claim 7 (depends on 1)

The agricultural work machine of claim 1, wherein the information map is based on sensor readings of one or more bands of electromagnetic radiation.

### Claim 8 (depends on 1)

The agricultural work machine of claim 1, wherein the information map comprises a yield map that includes, as the values of the first agricultural characteristic, yield values corresponding to the different geographic locations in the field and wherein the predictive agricultural model models, as the relationship, a relationship between crop state and yield based on the detected crop state value corresponding to the geographic location and a yield value in the yield map corresponding to the geographic location, the predictive agricultural model being configured to receive an input yield value as a model input and generate a predictive crop state value as a model output based on the modeled relationship.

### Claim 9 (depends on 1)

The agricultural work machine of claim 1, wherein the information map comprises a biomass map that includes, as the values of the first agricultural characteristic, biomass values corresponding to the different geographic locations in the field and wherein the predictive agricultural model models, as the relationship, a relationship between crop state and biomass based on the detected crop state value corresponding to the geographic location and a biomass value in the yield map corresponding to the geographic location, the predictive agricultural model being configured to receive an input biomass value as a model input and generate a predictive crop state value as a model output based on the modeled relationship.

### Claim 10 (depends on 1)

The agricultural work machine of claim 1 and further comprising:
a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive values of the second agricultural characteristic to the different geographic locations in the field based on the values of the first agricultural characteristic in the information map and based on the predictive agricultural model.

### Claim 11 (depends on 10)

The agricultural work machine of claim 10 and further comprising:
a control system configured to generate a control signal to control a controllable subsystem on the agricultural work machine based on the functional predictive agricultural map.

### Claim 12

A computer implemented method of generating a predictive agricultural model, the computer implemented method comprising:
obtaining an information map that includes values of a first agricultural characteristic corresponding to different geographic locations in a field,
detecting a geographic location of an agricultural work machine;
detecting, with an in-situ sensor, as a value of a second agricultural characteristic, a crop state value corresponding to the geographic location; and
generating a predictive agricultural model that models a relationship between the first agricultural characteristic and the second agricultural characteristic based on a value of the first agricultural characteristic in the information map corresponding to the geographic location and the detected value of the second agricultural characteristic corresponding to the geographic location.

### Claim 13 (depends on 12)

The computer implemented method of claim 12, and further comprising:
generating a control signal to control a controllable subsystem on the agricultural work machine based on the predictive agricultural model.

### Claim 14 (depends on 12)

The computer implemented method of claim 12, wherein obtaining the information map comprises obtaining, as the information map, a vegetative index map that includes, as the values of the first agricultural characteristic, vegetative index values corresponding to the different geographic locations in the field and wherein generating the predictive agricultural model comprises:
generating the predictive model modeling, as the relationship, a relationship between vegetative index and crop state based on the detected value of crop state corresponding to the geographic location and a vegetative index value in the vegetative index map corresponding to the geographic location; and
controlling a predictive model generator to generate the predictive agricultural model as being configured to receive an input vegetative index value as a model input and to generate a predictive crop state value as a model output based on the modeled relationship.

### Claim 15 (depends on 12)

The computer implemented method of claim 12, wherein obtaining the information map comprises obtaining, as the information map, a seeding characteristic map that includes, as the values of first agricultural characteristic, values of a seeding characteristic corresponding to the different geographic locations in the field and wherein generating the predictive agricultural model comprises:
generating the predictive model modeling, as the relationship, a relationship between crop state and the seeding characteristic based on the detected value of crop state corresponding to the geographic location and a value of the seeding characteristic in the seeding characteristic map corresponding to the geographic location; and
controlling a predictive model generator to generate the predictive agricultural model as being configured to receives an input value of the seeding characteristic as a model input and generate a predictive crop state value as a model output based on the modeled relationship.

### Claim 16 (depends on 12)

The computer implemented method of claim 12, wherein obtaining the information map comprises obtaining, as the information map, an information map that includes values of the first agricultural characteristic corresponding to the different geographic locations in the field and is derived from sensor readings of visible light.

### Claim 17 (depends on 12)

The computer implemented method of claim 12 and further comprising:
generating a functional predictive agricultural map of the field that maps predictive values of the second agricultural characteristic to the different locations in the field based on values of the first agricultural characteristic in the information map and the predictive agricultural model; and
generating a control signal to control a controllable subsystem on the agricultural work machine based on the functional predictive agricultural map.

### Claim 18

An agricultural-work system comprising:
a communication system configured to obtain an information map that includes values of a first agricultural characteristic corresponding to different geographic locations in a field;
a geographic position sensor configured to detect a geographic location of the agricultural work machine;
an in-situ sensor configured to detect, as a value of a second agricultural characteristic, a crop state value corresponding to the geographic location; and
a predictive model generator configured to generate a predictive agricultural model that models a relationship between the first agricultural characteristic and the second agricultural characteristic based on a value of the first agricultural characteristic in the information map corresponding to the geographic location and the detected value of the second agricultural characteristic corresponding to the geographic location.

### Claim 19 (depends on 18)

The agricultural system of claim 18 and further comprising:
a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive values of the second agricultural characteristic to the different geographic locations in the field based on the values of the first agricultural characteristic in the information map and based on the predictive agricultural model; and
a control system that generates a control signal to control a 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 information map is based on sensor readings of visible light reflected from crop plants at the worksite.