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

Nathan Greuel

John M Hageman

Stephen R Corban

Bradley K Yanke

Michael A Waldo

Sebastian Blank

Colin D Engel

Aaron J Bruns

Brian Joseph Gilmore

David A Hanson

Timothy S Hunt

Troy M Heims

Douglas John Bollin

Alexander B Lassers

Wentao W Yu

Volker Fuchs

Andrea M Agarwal

Kevin A Thelen

Hrishikesh H Tupe

Andrew Wesley Keenan

Mehul P Bhavsar

Narendar Reddy Karedla

Paul Readel

Bradley Lan

Travis J Auderer

An agricultural harvesting system obtains a yield map that maps yield values to different geographic locations in a worksite and a speed map that maps agricultural harvester speed values to different geographic locations in the worksite. The agricultural harvesting system identifies a geographic location in the worksite at which the agricultural harvester will be full, at least to a threshold level, based on the yield map; identifies a geographic location in the worksite at which a material transfer operation is to start based on the geographic location at which the agricultural harvester will be full, at least to the threshold level; and identifies a time at which the agricultural harvester will arrive at the material transfer location, based on the speed map. The agricultural harvesting system can control one or more of the agricultural harvester and a receiving machine.

Systems and methods for predictive harvesting logistics

Control zones are identified on a thematic map and work machine actuator settings are identified for each control zone. A position of the work machine is sensed and actuators on the work machine are controlled based on the control zone that the work machine is in, and based upon the actuator settings corresponding to the control zone. The control zone is then divided, on a display, into a harvested portion of the control zone on which an observed condition value is shown, and control zone that has yet to be harvested, on which an estimated value of the condition is shown.

Mobile work machine control based on control zone map data

An agricultural harvesting system includes a harvesting logistics module that is configured to receive a map that maps values of a crop characteristic to different geographic locations in a field. The harvesting logistics module further configured to identify a crop characteristic threshold and to identify a mixture of crop material based on the map and based on the crop characteristic threshold. The harvesting logistics module configured to generate a route for a mobile machine, such as a receiving machine or a harvester, based on the identified mixture.

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 agricultural model and map generation

Predictive map generation and control system

Predictive power map generation and control system

Machine control using a predictive map

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

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Bettendorf, IA, United States of America

Bhanu Kiran Reddy Palla