Abstract: A control system of an agricultural machine for performing a harvest operation includes a controller having a memory unit and a processor. The control system also includes a fuel sensor, a grain loss sensor and at least one user control of a plurality of user controls disposed in communication with the controller. The memory unit includes a plurality of instructions stored thereon that, in response to execution by the processor, causes the control system to receive a plurality of inputs via the controller including the rate of fuel from the fuel sensor, the amount of grain loss from the grain loss sensor, and one or more preset cost values from the at least one user control, process the plurality of inputs to determine a current cost of harvest value, and output a current cost of harvest value during the harvest 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: A grain tailings elevator for a combine harvester includes a an elevator housing having an interior containing a conveyor arrangement configured to transport grain tailings through the elevator housing to a discharge outlet. The elevator housing has a side wall with a window to the interior of the elevator housing. A camera having a camera housing containing an image sensor is mounted to the side wall of the elevator housing over the window with the image sensor in registration with the window. The image sensor is trained on the conveyor arrangement and configured to image the grain tailings transported by the conveyor arrangement through the elevator housing.

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 vehicle connection guidance system may include a sensor and a controller. The sensor is configured to be supported by a vehicle having a first width and a connection interface, the connection interface having a second width different than the first width and along which multiple connection points lie, wherein the sensor is to output steering angle signals. The Controller is configured to output presentation signals based upon the steering angle signals The presentation signals are to generate a visual presentation of a projected path of the width of the connection interface to an operator of the vehicle.

Abstract: Systems and methods for monitoring a weight of agricultural material, such as grain, unloaded from a first vehicle, such as a harvester (e.g., a combine harvester), to a second vehicle, such as a grain cart, and stopping transfer of the agricultural material when a sensed weight of the unloaded agricultural material contained in the second vehicle meets or exceeds a selected amount are disclosed. Example systems and methods may also include monitoring a fill level of agricultural material unloaded into the second vehicle simultaneously with monitoring the weight of unloaded agricultural material.

Abstract: Embodiments of a kernel-level grain monitoring system include a grain camera positioned to capture bulk grain sample images of a currently-harvested grain taken into and processed by a combine harvester, a moisture sensor, and a display device. A controller architecture is coupled to the grain camera, to the moisture sensor, and to the display device. The controller architecture is configured to: (i) analyze the bulk grain sample images, as received from the grain camera, to determine an average per kernel (APK) volume representing an estimated volume of a single average kernel of the currently-harvested grain; (ii) repeatedly calculate one or more topline harvesting parameters based, at least in part, on the determined APK volume and the moisture sensor data; and (iii) selectively present the topline harvesting parameters on the display device for viewing by an operator of the combine harvester.

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: A harvester may include a feeder house, a header mount interface proximate the feeder house; a header removably mounted to the feeder house by the header mount interface, the header having a mount interface for securing the header to a trailer; a sensor to output steering angle signals indicative of a steering angle of the harvester during an approach of the harvester carrying the header towards the trailer, and a controller to output control signals causing the display to overlay a representation of the mount interface on a real-time view of the approach.

Abstract: A vehicle attachment carrier loading guidance system may include a sensor for being supported by vehicle supporting a removable attachment having a mount interface for interacting with a mount on an attachment carrier distinct from the vehicle, wherein the sensor is output steering angle signals. The system may further comprise a controller to output presentation signals based upon the steering angle signals. The presentation signals are to generate a visual representation of a projected path of the mount interface with respect to the mount.

Abstract: A method and system for controlling the quality of harvested grains include capturing, by one or more image sensors, one or more images of material at a sampling location within a grain elevator of the combine harvester. The captured images are defined by a set of image pixels represented by image data and having a classification feature indicative of grain or non-grain material. One or more controllers receive the image data associated with the one or more images captured by the image sensor(s) and select a sample image defined by a subset of image pixels of the set of image pixels. The controller(s) apply a convolutional neural network (CNN) algorithm to the image data of the subset of image pixels of the selected sample image to determine the classification feature. The controller(s) analyze the determined classification feature to adjust an operational parameter of the combine harvester.

Abstract: A vehicle connection guidance system may include a sensor and a controller. The sensor is configured to be supported by a vehicle having a first width and a connection interface, the connection interface having a second width different than the first width and along which multiple connection points lie, wherein the sensor is to output steering angle signals. The Controller is configured to output presentation signals based upon the steering angle signals The presentation signals are to generate a visual presentation of a projected path of the width of the connection interface to an operator of the vehicle.

Abstract: A vehicle attachment carrier loading guidance system may include a sensor for being supported by vehicle supporting a removable attachment having a mount interface for interacting with a mount on an attachment carrier distinct from the vehicle, wherein the sensor is output steering angle signals. The system may further comprise a controller to output presentation signals based upon the steering angle signals. The presentation signals are to generate a visual representation of a projected path of the mount interface with respect to the mount.

Abstract: A method and system for controlling the quality of harvested grains include capturing, by one or more image sensors, one or more images of material at a sampling location within a grain elevator of the combine harvester. The captured images are defined by a set of image pixels represented by image data and having a classification feature indicative of grain or non-grain material. One or more controllers receive the image data associated with the one or more images captured by the image sensor(s) and select a sample image defined by a subset of image pixels of the set of image pixels. The controller(s) apply a convolutional neural network (CNN) algorithm to the image data of the subset of image pixels of the selected sample image to determine the classification feature. The controller(s) analyze the determined classification feature to adjust an operational parameter of the combine harvester.

Abstract: A grain tailings elevator for a combine harvester includes a an elevator housing having an interior containing a conveyor arrangement configured to transport grain tailings through the elevator housing to a discharge outlet. The elevator housing has a side wall with a window to the interior of the elevator housing. A camera having a camera housing containing an image sensor is mounted to the side wall of the elevator housing over the window with the image sensor in registration with the window. The image sensor is trained on the conveyor arrangement and configured to image the grain tailings transported by the conveyor arrangement through the elevator housing.