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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

Brandon M McDonald

Amer A Hassan

Christian F Huitema

Richard Joseph Connell

Nikolai R Tevs

Abhishek Abhishek

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

Thomas W Kuehnel

Jiandong Ruan

Joshua D Hoffman

Mark E Barker

Alexandru Gavrilescu

Stephen Michael Faivre

Curtis R Hammer

Bryan Kirk Buerkle

Francis Duong

William D Todd

Russell James Thacher

Alan David Sheidler

Duane M Bomleny

Cary S Hubner

Taroon Mandhana

Zachary Thomas Bonefas

Frederick William Nelson

Aaron J Bruns

Bernard D Aboba

Bernard Edwin Romig

Christopher D Turner

Jeffrey E Runde

Alok Manchanda

Samer Fouad Sawaya

Sharad Mittal

Sean A Mahrt

Jonathan Louis Tolstedt

Bradley J Hitchler

Hui Shen

Todd R Manion

Richard Allen Humpal

James Z Liu

Mitesh K Desai

Alireza Dabagh

Robert Thomas Casper

Scott Manchester

Robert D Donner

David A Roberts

Andrew Thomas Baron

Jeremy Lee Dewey

Volker Fuchs

Bradley John Meyer

Nicholas Lloyd Butts

Stephen R Corban

Rohit Gupta

Nathan E Krehbiel

Travis J Davis

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Travis Gene Funseth

Julian Sanchez

Anirban Banerjee

Deon Brewis

David A Hanson

Narasimha Rao S S Nagampalli

Gianluigi Nusca

Ross Carlyle Teague

Scott A Senkeresty

Cole L Murray

Craig Alan Puetz

Saurabh Mahajan

Gary S Keys, Ii

Colin D Engel

Eliot John Flannery

Douglas S Prairie

Aaron Matthew Senneff

Alexander B Lassers

Christopher Allen Myers

Jeffrey S Thompson

Mahesh Somarowthu

Tyler S Brammeier

Alex D Foessel

A sensor senses wind direction and generates a sensor signal. A wind processor processes the sensor signal to identify a wind direction at a location of an agricultural harvester. An action signal is generated to control the agricultural harvester to avoid discharging residue from the agricultural harvester into unharvested crop, based upon the wind direction.

Residue spread control

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

A geographic position of an agricultural machine is captured. Agricultural data is received that corresponds to a geographic position. Georeferenced visual indicia are displayed that are indicative of the received agricultural data.

Obtaining and augmenting agricultural data and generating an augmented display

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

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

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

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Noel Wayne Anderson