Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In accordance with an example embodiment, a vehicle path planning system and a method for planning a path of a vehicle having a work tool are provided. The method includes determining an actual path through a work area, modifying the actual path with a margin to determine a modified path plan through the work area, and passing the work tool through the work area with the vehicle along the modified path plan.

Abstract: 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.

Abstract: 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.

Abstract: Historical and seasonal data is obtained by an agricultural work machine. The historical data provides historical values of agricultural characteristics, which may or may not be geolocated, and the seasonal data provides seasonal values of agricultural characteristics corresponding to a current season. A predictive map generator generates a predictive map that predicts an agricultural characteristic, such as crop state, at different locations in the field based on a relationship between the historical values of agricultural characteristics in the historical data and based on the seasonal values of agricultural characteristics in the seasonal data at those different locations. The predictive map can be output and used in automated machine control.

Abstract: A system for spraying plants comprises a location-determining receiver for estimating a position of a sprayer with respect to one or more rows of plants. A distance sensor is arranged to measure a distance between a nozzle assembly and a plant row segment. A guidance module is adapted to align the nozzle assembly with a target path between the rows of plants, such as a centered path between the rows, or offset between the rows of the plants. A first nozzle is targeted toward a first zone with respect to the plant row segment based on a first spray pattern of the first nozzle. A second nozzle is targeted toward a second zone with respect to the plant row segment based on a second spray pattern of the second nozzle. A nozzle selection module for selecting automatically a first nozzle or a second nozzle based on maximum coverage of a target zone around the plant segment based on the first zone, the second zone, and the measured distance.

Abstract: A computer-implemented method of controlling a mobile agricultural machine includes obtaining prior field data representing a position of plants in a field, obtaining in situ plant detection data from operation of the mobile agricultural machine in the field, determining a position error in the prior field data based on the in situ plant detection data, and generating a control signal that controls the mobile agricultural machine based on the determined position error.

Abstract: 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.

Abstract: A geographic position of an agricultural machine is captured. Agricultural data is received that corresponds to a geographic position. Georeferenced visual indicia are displayed that are indicative of the received agricultural data.

Abstract: A geographic position of an agricultural machine is captured. Agricultural data is received that corresponds to a geographic position. Georeferenced visual indicia are displayed that are indicative of the received agricultural data.

Abstract: A geographic position of an agricultural machine is captured. Agricultural data is received that corresponds to a geographic position. Georeferenced visual indicia are displayed that are indicative of the received agricultural data.

Abstract: An object identification system on a mobile work machine receives an object detection sensor signal from an object detection sensor, along with an environmental sensor signal from an environmental sensor. An object identification system generates a first object identification based on the object detection sensor signal and the environmental sensor signal. Object behavior is analyzed to determine whether the object behavior is consistent with the object identification, given the environment. If an anomaly is detected, meaning that the object behavior is not consistent with the object identification, given the environment, then a secondary object identification system is invoked to perform another object identification based on the object detection sensor signal and the environmental sensor signal. A control signal generator can generate control signals to control a controllable subsystem of the mobile work machine based on the object identification or the secondary object identification.

Abstract: A device for detecting wear of a wear member composed of electrically resistive material. The wear member comprises at least two electrodes separated from each other, where each electrode overlies, or is embedded in, an outer surface of the electrically resistive material. One of the electrodes is connected to a resistor at a measurement node to form a resistive voltage divider. A voltage measurement device measures a change in voltage at the measurement node, where the change is voltage is indicative of the degree of removal of resistive material from a face of the wear member and where the change in voltage is continuously variable and not limited to discrete wear levels.

Abstract: Receivers are arranged to detect a corresponding observed phase shift, observed attenuation or other observed signal parameters for its respective microphone. An electronic data processor is adapted to estimate a distribution or quantity of material on the sieve based on the observed phase shift, the observed attenuation or the other observed signal parameters relative to a reference phase shift, a reference attenuation or other reference signal parameter. The operator can be alerted via a user interface if the material on the sieve is unevenly distributed or matches a preestablished distribution profile, or the sieve can be adjusted by an actuator to promote a generally uniform distribution.

Abstract: A device for detecting wear of a wear member composed of electrically resistive material. The wear member comprises at least two electrodes separated from each other, where each electrode overlies, or is embedded in, an outer surface of the electrically resistive material. One of the electrodes is connected to a resistor at a measurement node to form a resistive voltage divider. A voltage measurement device measures a change in voltage at the measurement node, where the change is voltage is indicative of the degree of removal of resistive material from a face of the wear member and where the change in voltage is continuously variable and not limited to discrete wear levels.

Abstract: A terahertz frequency-based sensing system for an agricultural harvester is provided. The system includes a terahertz sensor mounted to the agricultural harvester. The terahertz sensor at least one a terahertz source disposed to direct electromagnetic radiation toward a harvest material of the agricultural harvester. At least one terahertz detector is disposed to detect the terahertz electromagnetic radiation after the terahertz electromagnetic radiation interacts with the harvest material. A controller is operably coupled to the at least one terahertz detector and is configured to detect at least one harvest-related parameter based on a signal from the at least one terahertz detector and to perform an action based on the at least one detected parameter.

Abstract: An implement management system detects implement wear and monitors implement states to modify operating modes of a vehicle. The system can determine implement wear using the pull of the implement on the vehicle, the force and angle of which is represented by an orientation vector. The system may measure a current orientation vector and determine an expected orientation vector using sensors and a model (e.g., a machine learned model). Additionally, the implement management system can determine an implement state based on images of the soil and the implement captured by a camera onboard the vehicle during operation. The system may apply different models to the images to determine a likely state of the implement. The difference between the expected and current orientation vectors or the determined implement state may be used to determine whether and how the vehicle's operating mode should be modified.

Abstract: An implement management system detects implement wear and monitors implement states to modify operating modes of a vehicle. The system can determine implement wear using the pull of the implement on the vehicle, the force and angle of which is represented by an orientation vector. The system may measure a current orientation vector and determine an expected orientation vector using sensors and a model (e.g., a machine learned model). Additionally, the implement management system can determine an implement state based on images of the soil and the implement captured by a camera onboard the vehicle during operation. The system may apply different models to the images to determine a likely state of the implement. The difference between the expected and current orientation vectors or the determined implement state may be used to determine whether and how the vehicle's operating mode should be modified.