Deere & Company

Iowa State University Research Foundation Inc

United States of America, As Represented by the Secretary of Agriculture

Carnegie Mellon University

Matthew J Darr

Aaron J Bruns

Robert McNaull

James Joseph Phelan

Federico Pardina-Malbran

Jeremiah K Johnson

Herman Herman

Carlos Vallespi-Gonzalez

Zachary Thomas Bonefas

Adam D Sporrer

Niels Dybro

Mark Alan Cracraft

Bryan E Dugas

Jesse D Haecker

Hernan Badino

Jeffrey C Askey

Kevin Laverne Ehrecke

Daniel Joseph Corbett

John P Kruckeberg

Joseph Purswell

Berry D Lott

Thomas M Alferink

Kyle R Blaylock

Justin Borgstadt

A predictive map including predictive values of an agricultural characteristic of a worksite is obtained. An in-situ value of the agricultural characteristic is identified based on a sensor signal provided by an in-situ sensor. A corrected predictive map, including corrected predictive values of the agricultural characteristic, is generated based on the in-situ value of the agricultural characteristic. A controllable subsystem of a work machine is controlled based on the location of the work machine and the corrected predictive map.

Predictive map and machine control

One or more techniques and/or systems are disclosed for a center frame positioning method for an agricultural sprayer. The method comprises activating a center frame positioning system and collecting and processing position data related to a position of a suspended center frame in the center frame positioning system. The method further comprises evaluating the position data to determine whether any adjustment to the position of the suspended center frame is needed and controlling actuator force in at least one actuator to adjust the position of the suspended center frame based on the evaluating the position data.

Agricultural sprayer active boom center frame positioning system

A vegetation index characteristic is assigned to an image of vegetation at a worksite. The vegetation index characteristic is indicative of how a vegetation index value varies across the corresponding image. The image is selected for predictive map generation based upon the vegetation index characteristic. The predictive map is provided to a harvester control system which generates control signals that are applied to controllable subsystems of the harvester, based upon the predictive map and the location of the harvester.

Image selection for machine control

A system is provided that can include a 3D sensor. The 3D sensor can be configured to detect an area of an elevator on a harvester. The 3D sensor can further be configured to transmit a first signal associated with the area. The system can also include a processing device in communication with the 3D sensor. The system can further include a memory device in which instructions executable by the processing device are stored for causing the processing device to receive the first signal and determine a volume of a material on the elevator based on the first signal.

Yield measurement and base cutter height control systems for a harvester

A method and apparatus estimate yield. A first signal is received that an aggregate yield measured by an aggregate yield sensor during a measurement interval. A second signal is received that indicates a plurality of geo-referenced regions across which a harvester has traveled prior to the measurement interval. The method and apparatus allocate, to each of at least two geo-referenced regions, an aggregate yield portion allocation based upon different travel times for crops to the aggregate yield sensor Visual-Infrared Vegetative Index data derived from sensing of plants in selected portions of the electromagnetic spectrum at a time other than harvest. The aggregate yield portion allocations are output.

Yield estimation

A harvester generates a round module and positions the round module on a handler. The handler includes a cylinder having a base end and a rod end. A differential pressure across the cylinder is detected. A round module weight is generated based on the detected differential pressure.

Round module weighing using differential pressure sensing

A harvester includes an auger tube having a first portion and a second portion adjacent the first portion, an auger flight within the first portion of the tube and terminating prior to the second portion to move grain to the second portion, a window along the second portion of the tube and a camera to capture images of grain within the second portion of the tube. A computing device determines grain mass flow based upon the captured images, a dimension of the second portion of the tube and a grain density factor.

Grain mass flow rate determination

A method of calibrating a mass flow sensor while harvesting grain includes sensing an accumulated mass of a portion of grain within the grain tank with a first sensor. A mass flow rate sensor is calibrated based at least in part on a signal of the first sensor.

Grain mass flow estimation

Calibration of grain mass measurement

A crop yields sensing system comprises a first sensor of a first type associated with a portion of a harvester to output first signals facilitating determination of crop yield and a second sensor of a second type different than the first type to output second signals facilitating determination of crop yield for the portion of the harvester. The system further comprises a processing unit to receive the first signals and the second signals and determine crop yield for the portion of the harvester based on a combination of the first signals and the second signals.

Multi-sensor crop yield determination

A system and method for transferring agricultural material from a transferring vehicle to a storage portion of a receiving vehicle, and detecting and filling void areas of the agricultural material is disclosed. A transferring vehicle having a spout transfers agricultural material to a storage portion of a receiving vehicle. An imaging device associated with the transferring vehicle and facing towards the storage portion of the receiving vehicle collects imaging data. An image processing module is operable to, based on the imaging data, estimate fill levels of agricultural material within the storage portion; identify an area of the storage portion as targeted to fill; and identify a first void within the targeted area. A controller in communication with the image processing module is configured to command the spout to a position to direct agricultural material to the first void.

Artificial intelligence for detecting and filling void areas of agricultural commodity containers

The automated humidity control system controls the humidity within a setter hall. The system continuously monitors the existing setter hall thermal conditions and electronically compares the existing conditions to computerized data describing the targeted conditions. The system then takes action to conform the existing condition to the targeted condition.

Growing community of inventors

Ames, IA, United States of America

Matthew J Darr