A sensor arrangement for detecting grains in a material stream that contains grains and non-grain components in a combine harvester (10) may include a conveying device. The conveying device may include an inlet and an outlet (74). A material stream may be formed in response to a rotating movement in the conveying device. The sensor arrangement may also include an electro-optical sensor arranged on the outer circumference of the conveying device and configured to view the material stream. The sensor arrangement may also include an electronic processing device for detecting grains in the material stream by using the signal from the electro-optical sensor. The electro-optical sensor may be arranged in the downstream region of the conveying device.

A loss sensor calibration system detects a calibration trigger and measures a distance of travel of a harvester. When the harvester is stopped, the loss sensor calibration system generates an output indicative of a location where a manual loss measurement is to be taken for harvested material loss, relative to the harvester. The loss sensor calibration system generates a measured value input actuator that can be actuated by an operator to input the measured loss value. The loss sensor calibration system generates a scale factor based upon the measured value and applies the scale factor to a sensor signal generated by a material loss sensor to obtain a scaled sensor signal. A control system generates control signals based upon the scaled sensor signal.

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.

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 and system are provided for determining a threshing loss in a threshing system. The method includes irradiating a crop sample downstream at least a portion of a threshing system with electromagnetic waves having a frequency in a range of 0.1-10 THz, measuring a reflection and/or a transmission of the electromagnetic waves by the crop sample, establishing, based on the measured reflection and/or transmission, an at least two-dimensional terahertz image of the crop sample, identifying at least one ear in the terahertz image, identifying at least one grain kernel in the identified ear, and determining the threshing loss based on the identified grain kernel.

A combine harvester separates crop into straw and chaff and weed seeds using a sieve, a chopping rotor with a spreading device and at least one weed seed devitalization section. The components can be operated in a first mode where both the first material and said second material are directed to the chopper and a second mode the first material is directed to the chopper inlet and the second material is directed to the WSD. A grain loss section is provided at the sieve including grain loss sensors for collecting unseparated grain falling through openings in a rear section of the sieve to provide an indication of grain loss, and the grain entering the grain loss section is transferred during the first mode from the grain loss section to the WSD.

The present disclosure includes devices methods and systems for detecting lost crop in a work machine operation. The method may include receiving ground speed data from one or more speed detection systems. The method may include receiving crop condition data from one or more feed data sensors. The method may include determining one or more crop condition states based at least in part on one or more of the ground speed data and the crop condition data. The method may include determining lost crop based at least in part on the one or more crop condition states. The method may include adjusting one or more combine operation parameters based at least in part on the determination of lost crop.