A hybrid drive system for an auxiliary load includes a hydraulic auxiliary power storage. This hydraulic auxiliary power storage has a plurality of hydraulic power storage units. The hybrid drive system further comprises an power exchange control system that selectively connects a predetermined selection of one or more of the hydraulic power storage units to the hydraulic auxiliary power unit; and controls the power exchange of the auxiliary power unit as a function of the operating period by the predetermined selection.

An intelligent fill level system includes a material unloading container having an interior and a material entryway and a plurality of perception sensors mounted along a rim of the entryway, each sensor configured to generate perception data representative of a profile of unloaded material in at least a portion of the interior. A data processing unit, which may for example be onboard the material unloading container, determines one or more multidimensional profile characteristics, such as for example a fill level, of the unloaded material within the material unloading container at least via fusion of the perception data from the plurality of perception sensors, and a communications unit, which may also for example be onboard the container, transmits output signals representative of the determined one or more multidimensional profile characteristics to one or more remote data processing units associated with respective material loading work machines.

A detector on a material transfer vehicle detects a haulage vehicle. A localization processor locates a receiving area of the haulage vehicle relative to the material transfer vehicle. A control signal is generated to control a position of the material transfer vehicle so that a material conveyance subsystem is positioned to convey material from the material transfer vehicle to the haulage vehicle.

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.

A harvesting machine includes: a crop tank; a measuring device that measures an amount of crop that has been stored in the crop tank; an unloader apparatus that discharges crop that has been stored in the crop tank; a device control part that executes precise measurement oriented device setting processing; a measurement control part including a precise measurement execution part that performs, precise measurement which involves the precise measurement oriented device setting processing, and a simplified measurement execution part that executes, simplified measurement; an operational instruction processing part that outputs a precise measurement instruction and a simplified measurement instruction in response to an operation performed by a manual operation device; and a measurement result recording part that rewrites a simplified measurement result recorded based on a preceding simplified measurement instruction, with a precise measurement result that is based on a succeeding precise measurement instruction.

A combine harvester includes a conveyance mechanism for conveying grains obtained by a thresher for threshing grain culms reaped from a field to a grain tank, a measurer (340) for measuring the amount of grain conveyed to the grain tank as a conveyed yield, a yield assignment calculator (631) for calculating a minimal section yield, which is a yield per minimal section, by assigning the conveyed yield to a minimal section in the field, a grain conveyance state detector (632) for detecting a grain conveyance state of the conveyance mechanism (7), a yield corrector (633) for correcting the minimal section yield in accordance with the grain conveyance state, and a yield distribution data generator (661) for generating yield distribution data that represents a yield distribution in the field, based on the minimal section yield.

A grain tank level sensing system for a combine which harvests grain. The combine includes an upwardly open grain tank fillable with harvested grain and a cover that is displaceable for closing off the open grain tank. An actuator displaces the cover between an open and close position and either single or dual ultrasonic sensors continuously detect the level of grain in the tank and generate a signal proportional to the level of grain in the grain tank. A control system receives the signal from the ultrasonic sensors and generates an indication of the level in the tank to a monitor and/or disables the actuator to initiate the closing function when the grain tank is full.

One or more information maps are obtained by an agricultural system. The one or more information maps map one or more characteristic values at different geographic locations in a worksite. An in-situ sensor detects a material dynamics characteristic value as a mobile machine operates at the worksite. A predictive map generator generates a predictive map that predicts a predictive material dynamics characteristic value at different geographic locations in the worksite based on a relationship between the values in the one or more information maps and the material dynamics characteristic value detected by the in-situ sensor. The predictive map can be output and used in automated machine control.

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.

A receiving vehicle is automatically identified and the number of times it is filled is automatically counted. Control signals can be generated based on the number of times the receiving vehicle is filled.