Predictive weed map and material application machine control

Patent metadata

Field	Value
Publication number	US-2022232816-A1
Application number	US-202217716350-A
Country	US
Kind code	A1
Filing date	Apr 8, 2022
Priority date	Feb 6, 2020
Publication date	Jul 28, 2022
Grant date	—

Abstract

A predictive map is obtained by an agricultural material application system. The predictive map maps predictive weed values at different geographic locations in a field. A geographic position sensor detects a geographic locations of an agricultural material application machine at the field. A control system generates a control signal to control the agricultural material application machine based on the geographic locations of the agricultural material application machine and the predictive map.

First claim

What is claimed is:

1 . An agricultural material application system comprising:
a geographic position sensor that detects a geographic location of a mobile material application machine at a field; a control system that:
receives a predictive map that maps predictive weed values to different geographic locations in the field; and
generates a control signal to control a controllable subsystem of the mobile material application machine based on the geographic location of the mobile material application machine and the predictive map.

2 . The agricultural material application system of claim 1 and further comprising:
an in-situ sensor that detects a weed value corresponding to a geographic location;
a predictive model generator that:
receives an information map that maps values of a characteristic corresponding to different geographic locations in the field; and
generates a predictive model indicative of a relationship between values of the characteristic and weed values based on the weed value detected by the in-situ sensor corresponding to the geographic location and a value of the characteristic in the information map corresponding to the geographic location; and

a predictive map generator that generates, as the predictive map, a functional predictive map of the field that maps predictive weed values to the different geographic locations in the field based on the values of the characteristic in the information map and based on the predictive model.

3 . The agricultural material application system of claim 2 wherein the weed value is indicative of one or more of weed presence, weed type, weed size, and weed intensity.

4 . The agricultural material application system of claim 2 , wherein the information map comprises a vegetative index map that maps vegetative index values to the different geographic locations in the field;
wherein the predictive model generator generates, as the predictive model, a predictive weed model that models a relationship between vegetative index values and weed values based on the weed value detected by the in-situ sensor corresponding to the geographic location and a vegetative index value in the vegetative index map at the geographic location to which the detected weed values corresponds, the predictive weed model being configured to receive a vegetative index value as a model input and generate a predictive weed value as a model output; and wherein the predictive map generator generates, as the functional predictive map, a functional predictive weed map that maps predictive weed values to the different geographic locations in the field based on the vegetative index values in the information map and based on the predictive weed model.

5 . The agricultural material application system of claim 2 , wherein the information map comprises an optical map that maps optical characteristic values to the different geographic locations in the field;
wherein the predictive model generator generates, as the predictive model, a predictive weed model that models a relationship between optical characteristic values and weed values based on the weed value detected by the in-situ sensor corresponding to the geographic location and an optical characteristic value in the optical map at the geographic location to which the detected weed values corresponds, the predictive weed model being configured to receive an optical characteristic value as a model input and generate a predictive weed value as a model output; and wherein the predictive map generator generates, as the functional predictive map, a functional predictive weed map that maps predictive weed values to the different geographic locations in the field based on the optical characteristic values in the information map and based on the predictive weed model.

6 . The agricultural material application system of claim 2 , wherein the information map comprises a weed map that maps weed values to the different geographic locations in the field;
wherein the predictive model generator generates, as the predictive model, a predictive weed model that models a relationship between weed values and weed values based on the weed value detected by the in-situ sensor corresponding to the geographic location and a weed value in the weed map at the geographic location to which the detected weed values corresponds, the predictive weed model being configured to receive a weed value as a model input and generate a predictive weed value as a model output; and wherein the predictive map generator generates, as the functional predictive map, a functional predictive weed map that maps predictive weed values to the different geographic locations in the field based on the weed values in the information map and based on the predictive weed model.

7 . The agricultural material application system of claim 1 , wherein the information map comprises two or more information maps, each of the two or more information maps mapping values of a respective characteristic to the different geographic locations in the field;
wherein the predictive model generator generates, as the predictive model, a predictive weed model indicative of a relationship between values of the two or more respective characteristics and weed values based on the weed value detected by the in-situ sensor corresponding to the geographic location and values of the two or more respective characteristics in the two or more information maps corresponding to the geographic location, the predictive weed model being configured to receives a value of each of the two or more respective characteristics as model inputs and generate a predictive weed value as a model output; and wherein the predictive map generator generates, as the functional predictive map, a functional predictive weed map that maps predictive weed values to the different geographic locations in the field based on the values of the two more respective characteristics in the two or more information maps and the predictive weed model.

8 . The agricultural material application system of claim 7 , wherein the controllable subsystem comprises a material application actuator and wherein the control signal controls the material application actuator to increase an amount of material applied by the material application machine based on the functional predictive weed map.

9 . The agricultural material application system of claim 7 , wherein the controllable subsystem comprises a material application actuator and wherein the control signal controls the material application actuator to decrease an amount of material applied by the material application machine based on the functional predictive weed map.

10 . The agricultural material application system of claim 7 , wherein the controllable subsystem comprises a material application actuator and wherein the control signal controls the material application actuator to deactivate or activate a component of the material application machine based on the functional predictive weed map.

11 . A method of controlling a mobile agricultural material application machine comprising:
receiving a predictive map of a field that maps predictive weed values corresponding to different geographic locations in the field; detecting a geographic location of the mobile agricultural material application machine at the field; controlling a controllable subsystem of the mobile agricultural material application machine based on the geographic location of the mobile agricultural material application machine and the predictive map.

12 . The method of claim 11 and further comprising:
receiving an information map that maps values of a characteristic to different geographic lo

Assignees

• Deere & Co

Inventors

• Vandike Nathan R
• Palla Bhanu Kiran Reddy
• Pardina-Malbran Federico
• Hanson David A
• Agarwal Andrea M
• Anderson Noel W

Classifications

• A01C7/0443

  Seed singulators · CPC title

• A01C7/0445

  Seed ejectors · CPC title

• A01C7/046

  with perforated seeding discs · CPC title

• A01C7/06

  Seeders combined with fertilising apparatus · CPC title

• A01C7/08

  Broadcast seeders; Seeders depositing seeds in rows · CPC title