Deere & Company

Concord Gmbh

Case Corporation

Microsoft Corporation

Noel Wayne Anderson

Nathan R Vandike

Bhanu Kiran Reddy Palla

Gurmukh H Advani

Larry Lee Hendrickson

Federico Pardina-Malbran

Niels Dybro

Michael L Rhodes

Sebastian Blank

Matthew T Wold

Benjamin M Smith

Terence Daniel Pickett

Dohn W Pfeiffer

Richard Joseph Connell

Nikolai R Tevs

Brandon M McDonald

Tyler David Schleicher

James Joseph Phelan

David August Johnson

Curtis A Maeder

Jeffrey Scott Puhalla

Kevin Park Cowles

Bradley K Yanke

Mark William Stelford

Ramanathan Sugumaran

Kartheek Karna

Cody W Best

Kirk Narum Eisenbeis

Joshua D Hoffman

Mark E Barker

Stephen Michael Faivre

Curtis R Hammer

Bryan Kirk Buerkle

William D Todd

Russell James Thacher

Thomas G Mueller

Michael A Waldo

Arnold Albert Taube

Alan David Sheidler

Frederick William Nelson

Zachary Thomas Bonefas

Aaron J Bruns

Bernard Edwin Romig

Christopher D Turner

Jeffrey E Runde

Jonathan Louis Tolstedt

Bradley J Hitchler

Robert George Thackray

Tammo Wagner

Ali Haidous

Charles J Schleusner

Amer A Hassan

Christian F Huitema

Abhishek Abhishek

Richard Allen Humpal

Duane M Bomleny

Cary S Hubner

Robert Thomas Casper

Volker Fuchs

Bradley John Meyer

Nicholas Lloyd Butts

Nathan E Krehbiel

Stephen R Corban

Travis J Davis

Andrew K Rekow

Travis Gene Funseth

Julian Sanchez

David A Hanson

James Z Liu

Ross Carlyle Teague

Cole L Murray

Craig Alan Puetz

Colin D Engel

Gary S Keys, Ii

Aaron Matthew Senneff

Douglas S Prairie

Alexander B Lassers

Jeffrey S Thompson

Christopher Allen Myers

Sean A Mahrt

Tyler S Brammeier

Alex D Foessel

Mahesh Somarowthu

Wentao W Yu

Uwe Vollmar

Daniel P Bruns

Timothy Allen Murray

Monte A Dickson

Ryan J King

Bhanu Kiran Palla

Nathan Ogden

Andrea M Agarwal

Jacob D Mayer

Kevin A Thelen

Nathan Greuel

Robert A Stevens

Brittany Sprabery

Mehul P Bhavsar

Mark D Moran

Duong M Le

An agricultural harvesting machine includes crop processing functionality configured to engage crop in a field, perform a crop processing operation on the crop, and move the processed crop to a harvested crop repository, and a control system configured to identify a weed seed area indicating presence of weed seeds, and generate a control signal associated with a pre-emergence weed seed treatment operation based on the identified weed seed area.

Agricultural harvesting machine with pre-emergence weed detection and mitigation system

A mobile agricultural machine includes a header configured to engage crop at a worksite and a controllable header actuator configured to drive movement of the header relative to a surface of the worksite. The mobile agricultural machine further includes a crop constituent sensor system configured to sense the crop and generate a crop constituent sensor signal indicative of a value of a constituent of the crop. The mobile agricultural machine further includes a control system configured to generate a control signal to control the mobile agricultural machine based on the detected value of the constituent of the crop.

Crop constituents and agricultural harvester control

A planter configured for planting a sentinel plant in a field. The planter comprises a first planting system that is coupled to the planter. The first planting system is configured for planting a first sentinel plant having a first sentinel plant characteristic. A global positioning system is communicatively coupled to the planter. The global positioning system is configured for generating a location signal indicative of a location of the planter. A control system is communicatively coupled to the planter. The control system is configured to receive the first sentinel plant characteristic, receive the location signal, receive a georeferenced field characteristic, and control the first planting system based on at least one of the first sentinel plant characteristic, the location, or the georeferenced field characteristic.

Method for locating and planting sentinel plants

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

A mobile agricultural machine obtains an agricultural characteristic map indicative of agricultural characteristics of a field, wherein the agricultural characteristic map is based on data collected at or prior to a first time. The mobile agricultural machine obtains supplemental data indicative of characteristics relative to the worksite, the supplemental data collected after the first time. An agricultural characteristic confidence output, indicative of a confidence level in the agricultural characteristics indicated by the agricultural characteristic map, is generated based on the agricultural characteristic map and the supplemental data. In some examples, an action signal is generated to control an action of the mobile agricultural machine based on the agricultural characteristic confidence output.

Agricultural characteristic confidence and control

Methods and system for minimizing soil compaction relating to operations performed by agricultural machines. The agricultural machines can be guided so that at least one engagement body travels along an existing track path that was established by a prior travel of the same or different agricultural machine. Further, travel plans can determine the sequence in which the agricultural machine(s) travel in areas in the field so that travel on the track path(s) can be initiated in drier regions in the field. Operation of agricultural machines can also be adjusted to minimize deviations of other agricultural machines from existing track paths. For example, a windrower can operate a diverter to deposit a windrow at an offset location, and with a non-linear pattern, that may limit a baler from deviating from an existing track path while collecting crop material from the windrow, while also assisting in forming uniformly shaped crop bales.

System and method for controlling soil compaction

A seeding machine moves over the ground and includes a frame, at least one hopper containing seeds to be planted and planting row units connected to the frame. Each of the row units includes a seed metering device that separates and guides individual seeds into a planting position, and a shaft that reciprocates with respect to the frame through a stroke path. The stroke path includes extension towards the ground to a lowered position and retraction away from the ground to a raised position. The shaft engages one of the seeds from the planting position while the shaft is near the raised position. The shaft moves the one of the seeds downward while the shaft extends toward the ground to create an opening in the ground by pressing the one of the seeds into ground while the shaft is near the lowered position.

Planter having a seeding system

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

An implement management system detects implement wear and monitors implement states to modify operating modes of a vehicle. The system can determine implement wear using the pull of the implement on the vehicle, the force and angle of which is represented by an orientation vector. The system may measure a current orientation vector and determine an expected orientation vector using sensors and a model (e.g., a machine learned model). Additionally, the implement management system can determine an implement state based on images of the soil and the implement captured by a camera onboard the vehicle during operation. The system may apply different models to the images to determine a likely state of the implement. The difference between the expected and current orientation vectors or the determined implement state may be used to determine whether and how the vehicle's operating mode should be modified.

Implement management system for implement wear detection and estimation

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

One or more processors identify a first set of candidate passes of a second vehicle at a work area to provide area coverage of the work area within an outer boundary of the work area and to avoid contact between one or more components of the second vehicle and plant rows at the work area, based on dimensions of the second vehicle and estimated positions of the plant rows at the work area. A user interface presents, from a first present position and a first present heading of the second vehicle, the identified first set of candidate passes for operator selection of one of the first set of candidate passes as a planned first pass guidance path of the second vehicle.

Method and system for planning a path of a vehicle

A system and a method are disclosed for determining a range of potential distances between a work machine and an object. The system receives an image captured by a camera on the work machine and identifies an object in the image. The system determines an angle between the camera and the object, a height associated with the object, and an uncertainty associated with the height. Based on the angle, the height, and the uncertainty, the system determines a range of potential distances between the work machine and the object. The shortest distance in the range is compared to a threshold distance for safe operation of the work machine. When the shortest distance in the range is less than the threshold distance, the system causes the work machine to perform a safety action.

Work machine distance prediction and action control

A map is obtained by an agricultural system. The map includes values of one or more characteristics at different geographic locations in a worksite. In-situ sensor data indicative of values of one or more crop constituents at cut heights is obtained as the mobile machine operates at the worksite. A predictive model generator generates a predictive model that models a relationship between values of the one or more crop constituents at cut heights and the values of the one or more characteristics in the map. A predictive map generator generates a predictive map that predicts values of the one or more crop constituents at two or more cut heights at different geographic locations in the worksite based on the predictive model. The predictive map can be output and used in automated machine control.

Crop constituents, predictive mapping, and agricultural harvester control

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Fargo, ND, United States of America

Noel Wayne Anderson