Deere & Company

Nathan R Vandike

Bhanu Kiran Reddy Palla

Noel Wayne Anderson

Duane M Bomleny

Federico Pardina-Malbran

Matthew T Wold

Nathan E Krehbiel

Stephen R Corban

Bradley K Yanke

Colin D Engel

Corwin Marcus Puryk

Cody W Best

Benjamin M Lovett

Darin Ledru Roth

Garrick William Herbst

Todd E Van Hal

Michael A Waldo

Nicholas A Ohrstrom

Nathan Greuel

Andrea M Agarwal

Alan David Sheidler

Volker Fuchs

Gurmukh H Advani

Michael T Meschke

Brian Joseph Gilmore

Andrew J Peterson

Alexander B Lassers

James Hershbarger

Adam J Donohoe

Bhanu Kiran Palla

Dinesh V Phatak

Mahesh S Bothe

David L Wells

Ryan Patrick Mackin

Cary S Hubner

Glenn Everett Pope

William Douglas Graham

Niels Dybro

Martin Rittershofer

David A Hanson

Mark Michael Chaney

Paul I Craig

Troy M Heims

Scott N Clark

Christopher J Faulkner

Jeremiah K Johnson

Steven Timothy Rieck

William D Todd

Jeffrey R Walter

Austin M Laugen

Nathaniel R Bernklau

Wentao W Yu

Kevin A Thelen

Nathan Ogden

Thomas Herlitzius

Brian Gorge

Jody I Eyestone

Mehul P Bhavsar

Hrishikesh H Tupe

Adam J Beals

Zachary A Meyers

Eric M Heien

Brian J Gilmore

Derek J Franke

Travis J Auderer

Martin Unterpaintner

Megan L Winchell

Michael W Derew

Christian Korn

Sebastian Müller

Stephan Kirstein

Hans W Schaeffer

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

A crop residue monitoring system may include a harvester, a camera to capture an image of crop residue generated by the harvester, an analytical unit to derive a value for a crop residue parameter of the crop residue based upon an optical analysis of the image and a control unit to adjust a subsequent field operation based upon the value of the crop residue parameter.

Crop residue based field operation adjustment

A residue distribution system includes a flow channel having a first lateral side and a second lateral side disposed. A spreader is positioned proximate a downstream end of the flow channel, and broadcasts the flow of the crop residue. A residue distributor is positioned within the flow channel and upstream of the spreader for directing the crop residue toward one of the first lateral side or the second lateral side of the flow channel to increase a mass of the crop residue that side of the spreader, whereby a discharge density of the crop residue on that side of the spreader is increased to overcome effects of a crosswind or an uphill side-slope on broadcast distribution of the crop residue.

Spreader distribution compensation through material flow manipulation

Predictive power map generation and control system

One or more information maps are obtained by an agricultural system. The one or more information maps map one or more characteristic values at different geographic locations in a worksite. An in-situ sensor detects a mass flow value as a mobile machine operates at the worksite. A predictive map generator generates a predictive map that maps predictive mass flow values or predictive yield values at different geographic locations in the worksite based on a relationship between the values in the one or more information maps and the mass flow value detected by the in-situ sensor or the yield value based on the detected mass flow value. The predictive map can be output and used in automated machine control.

Map based farming for windrower operation

One or more information maps are obtained by an agricultural system. The one or more information maps map one or more characteristic values at different geographic locations in a worksite. An in-situ sensor detects a weed value as a mobile machine operates at the worksite. A predictive map generator generates a predictive map that maps predictive weed values at different geographic locations in the worksite based on a relationship between the values in the one or more information maps and the weed value detected by the in-situ sensor. The predictive map can be output and used in automated machine control.

An agricultural harvesting system includes a control system. The control system identifies a predictive value of a power characteristic based on a relationship between the power characteristic and a characteristic. The control system generates a control signal to control a controllable subsystem of a mobile hybrid agricultural harvesting machine based on the predictive value of the power characteristic.

Systems and methods for predictive power requirements and control

A computer implemented method includes receiving a map that maps values of a crop characteristic to different locations across a field; receiving harvester route data indicative of a planned route of a harvester at the field; identifying a crop characteristic threshold; identifying a material transfer end location indicative of a location, along the planned route of the harvester, at which a material transfer operation between the harvester and a receiving machine is to end, based on the map, the planned route of the harvester, and the crop characteristic threshold; identifying a material transfer start location range indicative of a geographic area, along the planned route of the harvester, at which the material transfer operation is to start based on the material transfer end location; and generating a route for the receiving machine to travel based on the material transfer end location and the material transfer start location range.

Systems and methods for predictive harvesting logistics

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

An agricultural operation evaluation system for an agricultural work vehicle includes an offboard device configured to collect offboard feedback associated with the agricultural operation and location information; and a controller. The controller is configured to receive the offboard feedback and the location information for the offboard feedback from the offboard device; determine at least one offboard input parameter from the offboard feedback; receive machine data including at least one onboard input parameter and location information for the least one onboard input parameter; correlate the at least one offboard input parameter and the at least one onboard input parameter based on the location information for the at least one offboard input parameter and the location information for the at least one onboard input parameter; and determine one or more vehicle adjustments based on the correlated at least one offboard input parameter and at least one onboard input parameter.

Agricultural operation evaluation system and method

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Geneseo, IL, United States of America

Nathan R Vandike