Deere + Company

Case Corporation

Cnh America LLC

Dohn W Pfeiffer

Sebastian Blank

Noel Wayne Anderson

Curtis R Hammer

James Joseph Phelan

Gurmukh H Advani

Lutz Bischoff

Robert A Matousek

Dirk Weichholdt

Joshua D Hoffman

Mario L Donini

Douglas John Bollin

Philip Alan Harden

Cristian Dima

Klaus Adam Braunhardt

Bruce Alan Coers

Timothy Arthur Deutsch

John Franklin Reid

Daniel James Burke

Robert A Stevens

Oliver Klein

Kevin S Schwinn

Mark Charles DePoorter

Joshua J Wolters

Friedrich Lauer

Rico Guido Priesnitz

Merle R Gerber

Colin D Engel

Klaus Ernst Becker

Martin Bueermann

Norbert Pohlmann

Alex D Foessel

Robert W Hawkins

Mark C Depoorter

Dan Burke

Jerry Alan Sandau

David Alden Skinner

Raymond Manford Andes

Douglas J Bollin

Jerry A Sandau

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 crop loss correction system receives one or more crop loss sensor signals that are indicative of crop lost by a harvesting machine. A correction component receives context information from a set of context sensing components to identify a context of the harvesting machine. The correction component corrects the crop loss sensor signals, based upon the context information, to obtain a corrected loss signal indicative of the sensed crop loss, corrected based on the mobile machine context. The corrected loss signal is output to an output device.

Augmented crop loss sensing

A computer-implemented method includes obtaining a weed map of a field including a crop, the weed map representing weed plant locations on the field, identifying, based at least in part on the weed map, weed seed locations that represent presence of weed seeds on the field, and generating a control signal for a pre-emergence weed mitigation operation based on the weed seed locations.

Pre-emergence weed detection and mitigation system

One or more techniques and/or systems are disclosed for identifying traversability of a site. Site conditions of a target site affecting traversability can be identified using real-time or historical data. Soil conditions, weather conditions, moisture levels, and other factors may be used to determine whether a target site is traversable by a target piece of equipment. The traversability information can be identified for a target area, that comprises a target site, and a traversability map can be generated for the site. Target equipment specifications can be used to determine whether the target equipment may effectively traverse the target site based on the traversability map. Further, a traversability path may be generated for a piece of target equipment based on the traversability map, site conditions, and the equipment specifications. Additionally, site management may be enhanced using the site condition data to plan traversability of target equipment at the target site.

Systems and methods for site traversability sensing

A sensor arrangement for an agricultural vehicle includes a first electro-optical sensor including a first field of view having an optical axis, and a second electro-optical sensor including a second field of view having an optical axis. The first and second sensors are spaced apart from one another and oriented such that the optical axes of the two sensors intersect at a distance from the two sensors.

Sensor arrangement for an agricultural vehicle

A method performed by a harvesting machine control system comprises obtaining bias data indicative of a performance bias of a first harvesting machine relative to at least one other harvesting machine, receiving a settings input indicative of a request to set performance of the first harvesting machine to correspond to performance of the at least one other harvesting machine, based on the bias data and machine settings associated with the performance of the at least one other harvesting machine, determining adjusted machine settings for the first harvesting machine, and outputting a control instruction to the first harvesting machine based on the adjusted machine settings.

Control of settings on a combine harvester with bias removal

A performance dimension is selected, and a gap in machine performance, according to the selected dimension, is identified. A target value is identified to improve machine control according to the selected dimension. A dependent dimension, which depends on the selected dimension, is selected and a dependency indicator, that indicates a dependency of the dependent dimension on the selected dimension, is accessed to identify a value of the dependent dimension that will change if the machine is controlled so that the value of the selected dimension is moved from a current value to the target value. The change in value of the selected dimension, and the dependent dimension are aggregated to determine whether machine control should be modified so the value of the selected dimension moves toward the target value. If so, a corresponding control operation is identified, and control signals are generated to control the machine to perform the identified control operation.

Cross-dimension performance improvement in machine control

A system and method of controlling an agricultural machine includes successively recording signals from a first sensor sensing an agronomic parameter of a field during an operation of the machine in the field. The system and method of controlling an agricultural machine also includes successively recording signals from a second sensor sensing an operation parameter of the machine during the operation of the machine in the field. The signals of the first sensor and the second sensor are spatially overlaid. A respective zone in the field is determined from the overlaid signals. An actuator of the machine is controlled dependent on the determined zone in which the machine operates.

System for optimizing agricultural machine settings

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 performed by a control system for a work machine includes receiving machine data indicative of operating parameters on the work machine, receiving a set of performance scores indicative of relative machine performance in a set of different performance categories, each performance score being generated by a different performance score generator based on sensor data associated with the work machine, accessing a set of rules that each map one or more triggering conditions to a corresponding adjustment action on the work machine, identifying a set of potential adjustment actions by evaluating fulfillment of the set of rules based on the operating parameters and performance scores, and correlating each potential adjustment action to one or more of the performance categories, selecting a particular adjustment action, from the set of potential adjustment actions, based on an indication of a selected target performance category, and outputting a control instruction based on the particular adjustment action.

Machine control system using performance score based setting adjustment

A system for controlling an operative parameter of a harvesting platform of an agricultural harvesting machine comprises a sensor adapted to provide downed crop information indicative of a characteristic of downed crop in a field to be harvested and an electronic control unit adapted to control an operative parameter of at least one of a reel and a cutter bar table position of the harvesting platform based upon the downed crop information.

System for optimizing platform settings based on crop state classification

Machine-generated data is divided into its metadata (or contextual data) that is indicative of a data container, and the data values themselves that are indicative of sensed variables. The metadata is checked to obtain a container integrity indicator of whether the data is accurate based on characteristics of the data container. The actual data values are subjected to quality checking to determine whether they have been corrupted by inaccuracies. Data corresponding to inaccurate metadata or inaccurate data values themselves is quarantined, and correction logic attempts to correct any inaccurate data. Corrected data is output with the verified data.

Mobile machine-generated data correction

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Bettendorf, IA, United States of America

Dohn W Pfeiffer