Deere & Company

Bhanu Kiran Reddy Palla

Nathan R Vandike

Noel Wayne Anderson

Federico Pardina-Malbran

Duane M Bomleny

Matthew T Wold

Nathan E Krehbiel

Corwin Marcus Puryk

Cody W Best

John M Hageman

Stephen R Corban

Bradley K Yanke

Michael A Waldo

Nathan Greuel

Sebastian Blank

Colin D Engel

Aaron J Bruns

Brian Joseph Gilmore

David A Hanson

Timothy S Hunt

Douglas John Bollin

Troy M Heims

Alexander B Lassers

Wentao W Yu

Volker Fuchs

Andrea M Agarwal

Kevin A Thelen

Paul Readel

Mehul P Bhavsar

Narendar Reddy Karedla

Hrishikesh H Tupe

Bradley Lan

Travis J Auderer

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.

Predictive power map generation and control system

Machine control using a predictive map

Map generation and control system

Predictive map generation and control system

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.

Predictive weed map and material application machine control

A crop constituent value is sensed by a crop constituent sensor on an agricultural machine. The crop constituent value is distributed among subregions covered by the agricultural machine. A vegetative index-estimated crop constituent value is obtained for each of the subregions. A weighted crop constituent value is generated for each subregion based upon the distributed constituent value for each subregion and the vegetative index-estimated constituent value for that subregion. An action signal is generated based upon the weighted crop constituent value for the subregion.

Crop constituent sensing

An information map is obtained by an agricultural system. The information map maps values of a characteristic to different geographic locations in a field. An in-situ sensor detects machine setting values as a mobile machine moves through the field. A predictive map generator generates a predictive map that predicts the machine setting at different locations in the field based on a relationship between the values of the characteristic and the machine setting values detected by the in-situ sensor. The predictive map can be output and used in automated machine control.

Predictive machine setting map generation and control system

An information map is obtained by an agricultural system. The information map maps values of a characteristic at different geographic locations in a worksite. An in-situ sensor detects material consumption values as a mobile material application machine operates at the worksite. A predictive map generator generates a predictive map that maps predictive material consumption values at different geographic locations in the worksite based on a relationship between values of the characteristic in the information map and material consumption values detected by the in-situ sensor. The predictive map can be output and used in automated machine control.

Predictive material consumption map and control

An agricultural work machine includes a geographic position sensor that detects a geographic location of the agricultural work machine. An in-situ sensor detects a value of a dynamic response characteristic of the agricultural work machine corresponding to the geographic location. A predictive model generator generates a predictive model that models a relationship between the terrain feature characteristic and the dynamic response characteristic based on a value of the terrain feature characteristic in a prior information map at the geographic location and a value of the dynamic response characteristic sensed by the in-situ sensor at the geographic location. A predictive map generator generates a functional predictive dynamic response map of the field, that maps predictive values of the dynamic response characteristic to the different geographic locations in the field, based on the values of the terrain feature characteristic in the prior information map and based on the predictive model.

Predictive response map generation and control system

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Bettendorf, IA, United States of America

Bhanu Kiran Reddy Palla