Deere + Company

Iowa State University Research Foundation, Inc.

Carnegie Mellon University

James Joseph Phelan

Aaron J Bruns

Noel Wayne Anderson

Andrzej Kozicki

Dohn W Pfeiffer

Gerald Eric Rains

Robert C Funk

Zachary W Slavens

Herman Herman

James Z Liu

Donald Keith Landphair

Carlos Vallespi-Gonzalez

Frederick William Nelson

Carl Knox Wellington

Jeremiah K Johnson

Cristian Dima

Robert McNaull

Zachary Pezzementi

Joan Campoy

Hanke Boesch

Cason Robert Male

Larry W Ferguson

Michael L Rhodes

Terence Daniel Pickett

Jeffrey Scott Wigdahl

John M Hageman

Niels Dybro

Matthew J Darr

Victor S Sierra

Sebastian Blank

Tyler David Schleicher

Georg A Kormann

Lutz Bischoff

Robert E Fox

Lawrence D Green

Mark Lindsay Rentz

Jerry Bob Hall

Garn Farley Penfold

Gregory C Eckart

Donald Lee Goodrich

Samuel Santiago

Brian J Booth

Thomas Herlitzius

Wayne Farrior Smith

Kent Robert Hawk

Robert A Stevens

Andrze J Kozicki

Bill J Ransdell

Michael Tuchscherer

Jefrey S Wigdahl

Jens Teichmann

Andi Günther

Matt Darr

Carlos Vallespi

Cason Male

Carl Wellington

Zach Pezzementi

A sensing system bias is reduced across a first agricultural machine and a second agricultural machine. A collection of agronomic data is accessed, that is indicative of an estimated crop yield. The collection that is accessed, for example, includes at least a first set of data sensed by the first agricultural machine and a second set of data sensed by the second agricultural machine. In addition, the first and second sets of data can be scaled based on a yield correction factor. A bias between the scaled first set of data and the scaled second set of data is determined, and a smoothing operation is performed on the scaled first and second sets of data. For example, performing the smoothing operation can include generating a calibration correction factor based on the determined bias, removing the bias between the scaled first set of data and the scaled second set of data to obtain a corrected set of crop yield data, and using the calibration correction factor in sensing the first set of data on the first agricultural machine and the second set of data on the second agricultural machine.

Correcting bias in agricultural parameter monitoring

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

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 particulate matter impact sensor () for sensing impacts of particles () comprises a support layer (); and a sensing media layer () disposed in front of the support layer ().

Particulate matter impact sensor

A method and apparatus estimate yield. A first signal is received that indicates 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 and pre-harvest weighting data value differences amongst the at least two georeferenced regions. The aggregate yield portion allocations are output.

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 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 moisture sensor that has a drive electrode and a separate sense electrode is described. Both electrodes have surfaces that face the sensed material and the surfaces are co-planar. The drive electrode receives an excitation signal and generates an electric field that produces a current in the sense electrode. The current is indicative of moisture in the sensed material.

Agricultural moisture sensor with co-planar electrodes

A product distribution apparatus, shown in the form of an agricultural air seeder is disclosed having sensors to measure the mass flow through the distribution system, sensors to weigh the tank and the product therein, and sensors to measure the quantity of product in the tank. The sensors are used to measure the change in the quantity of product in the tank during a calibration process where the apparatus is operated over an area and the number of rotations of the meter are recorded. The data is then used to determine a mass flow rate per revolution of the meter. A monitoring method is also disclosed in which an 'area to empty' and 'product needed' to complete a field or task is displayed to the operator.

Method of calibrating the meter of a product distribution apparatus and monitoring method

An impact sensor is located in a distribution tower which divides the seed and/or nutrient flow into individual rows, and a second sensor provides a compensation signal dependent upon one or more variables such as the velocity of the air in the conveying system, implement vibrations. As the seed/fertilizer bounces off of the impact sensor and flows into the individual row air streams, the impact sensor provides a force signal to a processor which calculates the total particulate mass flow rate from the force signal and the air velocity signal. To determine individual seed and fertilizer rates, a rate controller temporarily changes the metering rate of one of the materials, and the processor then calculates the desired information from the mass flow change and meter speed change. Another embodiment includes seed sensor structure at the meter output for achieving or confirming accuracy.

Particulate flow sensing for an agricultural implement

A system for presenting a crop sample to a crop property sensor is in particular suited for a harvesting machine and comprises a bypass line branching off from a crop feeding assembly, a conveyor for feeding the branched-off crop through the bypass line without damaging the crop, and a crop property sensor for sensing one or more properties of the crop in the bypass line. The bypass line is upwardly angled or extends vertical, such that the conveyor elevates the material from the crop guiding channel to the crop property sensor.

Crop sample presentation system

A level sensor, associated with a bin, detects a particular reference level of an agricultural product in the bin and generates a calibration indicator. An input sensor determines an input rate of the agricultural product into the bin. A conveyer is capable of transporting the agricultural product out of the bin. An output sensor provides a status indicator that indicates whether the conveyer is in an active state or in an inactive state during one or more evaluation time windows. A data processor determines an output rate of the agricultural product based on the status indicator. The data processor comprises a level estimator for estimating an estimated bin level of the bin based on the input rate and an output rate. The data processor comprises a calibrator for calibrating the estimated bin level with reference to the particular reference level in response to the calibration indicator.

Monitoring of bin level for an agricultural product

A grain moisture sensing system having an excitation signal source for producing an excitation signal. A sensor cell having a driven plate and a sense plate that applies to the excitation signal for captive measurement between the driven plate and the sense plate to produce a current at the sense plate. Connected to the excitation source is a first synchronous detector and connected to the sense plate is a second synchronous detector.

Multiple frequency grain moisture sensor for combines

Growing community of inventors

Bettendorf, IA, United States of America

James Joseph Phelan