A grain loss detector disposed on a combine harvester to detect grain loss within crop material being discharged by a separating or cleaning stage component of combine harvester while harvesting a crop. The grain loss detector includes at sensor generating sensor data output. A computing device receives the sensor data output for processing via software which compares the sensor data output against crop data characteristics to identify any grain kernels from among material other than grain. In one embodiment the sensor is a camera and the generated sensor data output is a digital image frame capturing the grain kernels and material other than grain passing the camera.

Disclosed are various embodiments for using artificial intelligence to monitor harvest losses of combine harvesters. Images can be periodically captured from a ground-facing camera mounted to a combine harvester. An amount of gleanings can be counted in the image. An estimated amount of harvest loss is then calculated based at least in part on the amount of gleanings. The estimated amount of the harvest loss can then be displayed to a user or can be used as the basis for automatically adjusting the operation of the combine harvester.

A method for controlling header height comprising generating a signal as an object passes through a row unit; establishing a signal threshold, wherein when the signal threshold is exceeded a shelled ear event is detected; comparing the signal to the signal threshold; and emitting an alarm with the signal exceeds the signal threshold.

Disclosed herein are various devices, systems, and methods for use in agricultural, particularly for use in harvesting agricultural crop such as corn. Various implementations relate to devices, systems, and methods for increasing yield by emitting warnings when the corn head to not at the proper height.

A sensor arrangement for detecting grains in a material stream containing grains and non-grain components in a combine harvester may include a sampling device configured to collect a sample from the material stream; a separation device configured to separate the sample into grains and non-grain constituents; and a sensor system configured to sense the grains separated from the non-grain constituents.

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.

A first in-situ sensor detects a characteristic value as a mobile machine operates at a worksite. A second in-situ sensor detects a material dynamics characteristic value as the mobile machine operates at the worksite. A predictive model generator generates a predictive model that models a relationship between the characteristic and the materials dynamics characteristic based on the characteristic value detected by the first in-situ sensor and the material dynamics characteristic value detected by the second in-situ sensor. The predictive model can be output and used in automated machine control.

A crop loss monitoring method includes detecting crop loss in a measurement area, the measurement area corresponding to an unharvested area of a field. The method further includes identifying a pre-harvest crop loss value based on the crop loss detected in the measurement area. The method further includes generating one or more control signals based on the pre-harvesting crop loss value.

A combine harvester has a loss sensing system that senses loss and generates a signal indicative of the sensed loss. A metering system injects a known amount of additional material to be sensed by the loss sensing system. A calibration system compares the signal generated by the loss sensing system to the known amount of injected material and calibrates the loss sensing system based upon the known amount of injected material and the signal from the loss sensing system.

A method of operating an agricultural vehicle includes the steps of: generating active runtime data based on performance of the vehicle, receiving a signal to activate a kill-stall procedure of the vehicle, recording the active runtime data as recorded runtime data prior to kill-stalling the vehicle, and kill-stalling the vehicle.