Abstract: A kernel processor adjustment system and technique is provided. Material processed by a kernel processor is measured with a sensor assembly having one or more sensors. One or more properties are determined from sensor data and the properties are employed to determine a characteristic of the processed material. The characteristic is evaluated based on a performance metric. Based on the evaluation, adjusted settings of the kernel processor are determined. Control signals for the kernel processor are generated and communicated to execute the setting change.

Abstract: An agricultural machine includes a vibration stimulation source configured to generate a vibration stimulation signal directed toward plant matter and a sensor system configured to sense electromagnetic radiation reflected from the plant matter, generate a first signal based on the sensed electromagnetic radiation, and generate a second signal indicative of a resonant vibration response of the plant matter, that is in response to the vibration stimulation signal. The agricultural machine includes a plant evaluation system configured to, based on the first and second signals, generate plant characterization data indicative of one or more physical characteristics of the plant matter, and a control system configured to generate an action signal based on the plant characterization data.

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: 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: 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: One or more techniques and/or systems are disclosed for determining appropriate settings and controlling windrower performance based on the constituents of the target crop. Crop condition and constituents can be identified in the field using sensors in/at the windrower implement, such as constituent sensors and/or imaging sensors. An implement controller can receive sensor data from the sensor array and generate crop condition data indicative of a condition of the target crop. The controller can also generate actuator adjustment data indicative of an adjustment to the roller assembly in the windrower implement to meet a target crop dry-down characteristic. One or more actuators can be used to adjust the roller assembly of the windrower implement based at least on the adjustment data, which can be done automatically or by an operator to the roller systems in the implement.

Abstract: An agricultural residue distribution control system associated with an agricultural work vehicle includes one or more sensors configured to collect information of an initial state at a field location including an amount of biomass at the field location, soil characteristics at the field location, or both, during the initial state; and a controller. The controller is configured to: receive the information collected from the one or more sensors of the biomass, the soil characteristics, or both, at the field location during the initial state; acquire a target future state at the field location; generate a residue distribution plan for the target future state at the field location based on the received information collected from the one or more sensors of the amount of biomass, the soil characteristics, or both, at the field location during the initial state; and execute the residue distribution plan to achieve the target future state.

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 field planting system for planting seeds of at least two plant genotypes in a field includes a multi-variety planter operable to plant the seeds of the at least two plant genotypes in the field. The field planting system also includes a controller in operable communication with the multi-variety planter. The controller is configured to receive georeferenced field information for the field and plant information for the at least two plant genotypes, and generate a planting map for the field based on the georeferenced field information and the plant information, the planting map including a planting pattern for interplanting the seeds of the at least two plant genotypes. The controller is further configured to monitor a location of the multi-variety planter in the field, and control the multi-variety planter to plant the seeds of the at least two plant genotypes in the field according to the planting map.

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: One or more techniques and/or systems are disclosed for automatically adjusting conditioning rollers of a crimping implement, such as those used to condition hay or other crimped crops, such as cover crops. Adjustments can be made on the fly to rollers, crimpers, or down pressure applicators used to condition the crop based on several factors such as the condition of the crop, the type of crop, and more. Sensors can be used to detect the crops and others' conditions before and after conditioning, and automatic adjustments can be made, such as the distance between conditioning rollers, and/or the pressure applied by the rollers, etc. to meet a desired crop condition for the crimped crop.

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 data processor is configured to identify the component pixels of a harvestable plant component within an obtained image data of plant pixels of the one or more target plants. An edge, boundary or outline of the component pixels is determined. The data processor is configured to detect a size of the harvestable plant component based on the determined edge, boundary or outline of the identified component pixels, along with an adjustment based on analysis of any exposed portions of the harvestable plant component. A user interface is configured to provide a yield indicator based on the detected size of the harvestable plant component.

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: 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: 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: 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: One or more techniques and/or systems are disclosed for developing appropriate settings for a windrower implement or forage harvester implement, to obtain a desired crop conditioning profile for a target crop. Field crop conditions may vary due to soil conditions, moisture content, and the operator may wish to get crop uniformity when collected. As such, a profile can be developed that provides adjustment data to actuators to adjust machine setting, to provide for a conditioning systems to obtain a desired result. The adjustment data can be generated based on such as the moisture content of the target crop, the target moisture content, and the dry-down characteristics for the crop. The profile can help set the harvesting system to provide appropriate cut and crimp to result in a desired dry-down time to get uniformity in the collected crop.

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.