Abstract: A material transfer operation system includes one or more processors and memory storing instructions that, when executed by the one or more processors, configure the one or more processors to identify a material transfer machine material transfer route indicative of a route along which a material transfer machine is to conduct a material transfer operation; identify a material receiving machine material transfer route indicative of a route along which the material receiving machine is to conduct the material transfer operation; identify a material transfer machine slope value; identify a material receiving machine slope value; generate an adjustment based on the identified material transfer machine slope value and the material receiving machine slope value; and control one or more of the material transfer machine and the material receiving machine based on the adjustment.

Abstract: An agricultural system includes: one or more weight sensors that detect a weight of material in a first material receptacle and generate sensor data indicative thereof; one or more processors; memory; and computer executable instructions, stored in the memory, that, when executed by the one or more processors, configure the one or more processors to: determine a change in weight of material in the first material receptacle based on first sensor data indicative of a weight of material in the first material receptacle at a first time and second sensor data indicative of a weight of material in the first material receptacle at a second time; determine a material transfer rate indicative of a rate at which a material transfer subsystem transfers material based on the determined change in weight; and determine a fill level of material in a second material receptacle based on the determined material transfer rate.

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: 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: Data is obtained by a worksite operation system. The data includes machine sensor data indicative of one or more characteristics of a mobile machine operating at a worksite. The worksite operation system generates, based on the obtained data, a dynamic boundary output indicative of a predictive boundary of the mobile machine at a location along a predictive path of the mobile machine at the worksite. The worksite operation system generates a control signal based on the dynamic boundary output.

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: 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: A residue sensor is mounted to a combine harvester to sense a characteristic of residue. The residue sensor can be mounted to a spreader, to a sidewall of a chopper, or in a bypass system. The residue sensor can also be protected by an air curtain that directs air to inhibit contact between the residue sensor and the residue.

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 techniques and/or systems are disclosed for automating the collection of product harvested from a site, such as grain from a field, and filling truck trailer for transport off site. These techniques can help operations that want to improve efficiency, while reducing field compaction and other issues related to weather or environmental conditions. A predetermined level of product can be identified for the target truck trailer, and an optimized fill method can be determined for filling the truck trailer efficiently using one or more mobile transport containers moving product from the field. A sensor array can detect an amount of the product disposed in the mobile transport container during transfer, and used to fill the mobile transport container to a predetermined level based at least on the optimized fill method. The mobile transport container is then used to fill the target truck-trailer container to its predetermined level.

Abstract: One or more techniques and/or systems are disclosed for automating the collection of product harvested from a site, such as grain from a field, and filling truck trailer for transport off site. These techniques can help operations that want to improve efficiency, while reducing field compaction and other issues related to weather or environmental conditions. A predetermined level of product can be identified for the target truck trailer, and an optimized fill method can be determined for filling the truck trailer efficiently using one or more mobile transport containers moving product from the field. A sensor array can detect an amount of the product disposed in the mobile transport container during transfer, and used to fill the mobile transport container to a predetermined level based at least on the optimized fill method. The mobile transport container is then used to fill the target truck-trailer container to its predetermined level.

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.