Deere & Company

Federico Pardina-Malbran

Nathan R Vandike

Bhanu Kiran Reddy Palla

Noel Wayne Anderson

Michael A Waldo

Matthew T Wold

Cody W Best

David A Hanson

Matthew J Darr

Ramanathan Sugumaran

Andrea M Agarwal

Kevin A Thelen

Marc Lemoine

Travis J Auderer

A predictive map is obtained by an agricultural material application system. The predictive map maps predictive weed values at different geographic locations in a field. A geographic position sensor detects a geographic locations of an agricultural material application machine at the field. A control system generates a control signal to control the agricultural material application machine based on the geographic locations of the agricultural material application machine and the predictive map.

Predictive weed map and material application machine control

A crop constituent value is sensed by a crop constituent sensor on an agricultural machine. The crop constituent value is distributed among subregions covered by the agricultural machine. A vegetative index-estimated crop constituent value is obtained for each of the subregions. A weighted crop constituent value is generated for each subregion based upon the distributed constituent value for each subregion and the vegetative index-estimated constituent value for that subregion. An action signal is generated based upon the weighted crop constituent value for the subregion.

Crop constituent sensing

An information map is obtained by an agricultural system. The information map maps values of a characteristic at different geographic locations in a worksite. An in-situ sensor detects material consumption values as a mobile material application machine operates at the worksite. A predictive map generator generates a predictive map that maps predictive material consumption values at different geographic locations in the worksite based on a relationship between values of the characteristic in the information map and material consumption values detected by the in-situ sensor. The predictive map can be output and used in automated machine control.

Predictive material consumption map and control

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.

Map generation and control system

Crop state map generation and control system

An information map is obtained by an agricultural system. The information map maps values of a characteristic at different geographic locations in a worksite. An in-situ sensor detects nutrient values as a mobile material application machine operates at the worksite. A predictive map generator generates a predictive map that maps predictive nutrient values at different geographic locations in the worksite based on a relationship between values of the characteristic in the information map and nutrient values detected by the in-situ sensor. The predictive map can be output and used in automated machine control.

Predictive nutrient map and control

Predictive speed map generation and control system

A priori geo-referenced data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the geo-referenced data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.

Machine control using real-time model

A priori georeferenced vegetative index data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the georeferenced vegetative index data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.

A priori geo-referenced vegetative index data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the geo-referenced vegetative index data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.

A work machine receives a thematic map that maps values of a variable to different geographic locations at a worksite. Control zones are dynamically identified on the thematic map and actuator settings are dynamically identified for each control zone. A position of the work machine is sensed, and actuators on the work machine are controlled based upon the control zones that the work machine is in, or is entering, and based upon the settings corresponding to the control zone. These control zones and settings are dynamically adjusted based on in situ (field) data collected by sensors on the work machine.

Zonal machine control

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.

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Fort Collins, CO, United States of America

Federico Pardina-Malbran