Deere + Company

Iowa State University Research Foundation, Inc.

Carnegie Mellon University

Other

Aaron J Bruns

Glenn Everett Pope

James Joseph Phelan

Herman Herman

Matthew J Darr

Carlos Vallespi-Gonzalez

Niels Dybro

John M Hageman

Jeremiah K Johnson

Julian Sanchez

Troy K Maddox

Timothy S Hunt

Jeffery J Adams

Kelly L Sikkema

Margaux M Price

Robert McNaull

Victor S Sierra

Ryan M Gilsdorf

Noel Wayne Anderson

Zachary Thomas Bonefas

Brian Joseph Gilmore

Mark Michael Chaney

Carl Knox Wellington

Cristian Dima

Zachary Pezzementi

Joan Campoy

Hanke Boesch

Cason Robert Male

Jose Gonzalez Mora

Edward L Farwell

Bhanu Kiran Reddy Palla

Tarik Loukili

Chad Allen Dow

Vladimir Altman

Douglas John Bollin

William D French, Jr

Jaime W Bourne

Cameron R Mott

Stuart Birrell

Bhanu Kiran Palla

Stephen Michael Patton

Paul Readel

Anja Eggerl

Janos Kis

Brian J Gilmore

Matt Darr

Vladimir A Altman

Carlos Vallespi

Cason Male

Carl Wellington

Zach Pezzementi

An overall machine operational state (such as a problem state, field state, machine state, other non-problem state, etc.) is identified, and exit and entry conditions are monitored to determine whether the machine transitions into another operational state. When the machine transitions into a problem state, a multiple input, multiple output control system uses a state machine to identify the problem state and a solution is identified. The solution is indicative of machine settings that will return the machine to an acceptable, operational state. Control signals are generated to modify the machine settings based on the identified solution.

State machine for multiple input-multiple output harvester control

A method and non-transitory computer-readable medium capture an image of bulk grain and apply a feature extractor to the image to determine a feature of the bulk grain in the image. For each of a plurality of different sampling locations in the image, based upon the feature of the bulk grain at the sampling location, a determination is made regarding a classification score for the presence of a classification of material at the sampling location. A quality of the bulk grain of the image is determined based upon an aggregation of the classification scores for the presence of the classification of material at the sampling locations.

Grain quality monitoring

A machine control system receives sensor signals indicative of sensed variables on a mobile machine and calculates performance metric values for the mobile machine based upon the sensed variables. The machine control system displays information indicative of the performance metric value for the mobile machine on a time continuous user interface display.

User interface performance graph for operation of a mobile machine

An agricultural harvester () has a control system () that is configured to control a biomass harvesting rate based upon electronically calculated soil effects.

Agricultural harvester configured to control a biomass harvesting rate based upon soil effects

An apparatus may include a harvester head having a crop harvesting width, different components across the harvesting width to interact with plants being harvested, at least one sensor to sense a power characteristic associated with each of the different components and a processing unit to output a crop quantity distribution signal based upon differences in the power characteristic associated with the different components across the harvesting width.

Yield estimation

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.

Disclosed herein is a system for evaluating agricultural material which comprises, in one embodiment, a housing having a passage in or through an interior of the housing with an inlet for receiving agricultural material and an outlet for outputting the agricultural material. The system further comprises a wall opening in a wall of the passage. An imaging device having a lens is located inward from a border of the imaging device. The imaging device is pivotally mounted for rotation with respect to a housing such that in a closed state the border of the imaging device rests on, engages or interlocks the wall opening of the housing, and in an open state the border exposes the wall opening and an interior of the housing.

System for evaluating agricultural material

A harvester interacts with plants so as to separate a harvested portion from a biomass portion of each of the plants. At least one sensor carried by the harvester outputs a signal based upon an attribute of the biomass portion.

Biomass sensing

A method for automatically tuning an agricultural combine (), comprises the steps of: receiving () a signal from an operator of the agricultural combine () indicating that current operation of the agricultural combine () operation is acceptable; determining () current performance parameters of the agricultural combine after the step of receiving; calculating () a performance parameter error limit for each of the current performance parameters in the step of determining; again determining () current performance parameters; comparing () the again determined current performance parameters with the performance parameter error limits to thereby determine whether one or more of the again determined current performance parameters falls outside its associated performance parameter error limit; and if at least one of the again determined current performance parameters falls outside its associated performance parameter error limit, then calculating () changes to machine settings of the agricultural combine () that will bring the at least one of the again determined current performance parameters back within its associated performance parameter error limit.

Automatic tuning of an intelligent combine

A method and apparatus estimate yield. A first method comprises receiving a first signal indicating an aggregate yield measured by an aggregate yield sensor during a measurement interval, receiving a second signal indicating a plurality of geo-referenced regions across which a harvester head has traveled prior to the measurement interval, allocating a portion of the aggregate yield to each of at least two geo-referenced regions based upon different travel times for crops from different portions of the head and outputting aggregate yield portion allocations. A second method estimates yield based upon sensed power characteristics across the harvesting width of a harvesting machine.

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 set of sensor inputs are received in an agricultural machine. The sensor inputs are indicative of sensed or measured variables. A probabilistic control system probabilistically infers values for another set of variables and generates a set of control signals based on the probabilistically inferred values.

Probabilistic control of an agricultural machine

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

Growing community of inventors

Bettendorf, IA, United States of America

Aaron J Bruns