US 12,422,847 B2
Predictive agricultural model and map generation
Nathan R Vandike, Geneseo, IL (US); Bhanu Kiran Reddy Palla, Bettendorf, IA (US); Noel W. Anderson, Fargo, ND (US); and Duane M. Bomleny, Geneseo, IL (US)
Assigned to Deere & Company, Moline, IL (US)
Filed by Deere & Company, Moline, IL (US)
Filed on Oct. 9, 2020, as Appl. No. 17/067,551.
Prior Publication US 2022/0113729 A1, Apr. 14, 2022
Int. Cl. G01C 21/00 (2006.01); B60K 35/00 (2024.01); G05D 1/00 (2024.01); B60K 35/22 (2024.01); B60K 35/28 (2024.01)
CPC G05D 1/0212 (2013.01) [B60K 35/00 (2013.01); G01C 21/3826 (2020.08); G01C 21/3889 (2020.08); G05D 1/0094 (2013.01); B60K 35/22 (2024.01); B60K 35/28 (2024.01); B60K 2360/166 (2024.01); G05D 1/0231 (2013.01); G05D 1/0257 (2013.01)] 19 Claims
OG exemplary drawing
 
1. An agricultural work machine, comprising:
a communication system that receives an information map that includes values of a first agricultural characteristic corresponding to a set of locations in a field;
an in-situ sensor that detects a value of a second agricultural characteristic, different than the first agricultural characteristic, corresponding to a first location of the set of locations in the field, wherein the second agricultural characteristic comprises a header characteristic;
one or more processors; and
a data store that stores computer executable instructions 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 values of the first characteristic and values of the second characteristic based, at least, on a value of the first agricultural characteristic, in the information map, corresponding to the first location and the detected value of the second agricultural characteristic corresponding to the first location;
map a predictive value of the second agricultural characteristic at a second location of the set of locations in the field based on a value of the first agricultural characteristic, in the information map, corresponding to the second location and the predictive agricultural model; and
control a controllable subsystem based on the mapped predictive value of the second agricultural characteristic corresponding to the second location.