In one example, a system for evaluating an agricultural material is provided. The system comprising: a housing having a passage in or through an interior of the housing with an inlet for receiving agricultural material and an outlet for outputting the agricultural material; a wall opening in a wall of the passage; and an imaging device having a window located within a border, the imaging device having a removable portion with at least one of a moisture absorbing material, a heat source, and an anti-fog agent within the removable portion.

A terahertz frequency-based sensing system for an agricultural harvester is provided. The system includes a terahertz sensor mounted to the agricultural harvester. The terahertz sensor at least one a terahertz source disposed to direct electromagnetic radiation toward a harvest material of the agricultural harvester. At least one terahertz detector is disposed to detect the terahertz electromagnetic radiation after the terahertz electromagnetic radiation interacts with the harvest material. A controller is operably coupled to the at least one terahertz detector and is configured to detect at least one harvest-related parameter based on a signal from the at least one terahertz detector and to perform an action based on the at least one detected parameter.

A method and a system for determining an indicator of processing quality of an agricultural harvested material using a mobile device is disclosed. A computing unit analyzes image data of a prepared sample of harvested material containing grain components and non-grain components in an analytical routine to determine the indicator of the processing quality of the agricultural harvested material. Further, the computing unit uses a trained machine learning model in the analytical routine to perform at least one step of determining the indicator of the processing quality of the agricultural harvested material and that the computing unit adjusts at least one machine parameter of the forage harvester based on the indicator of processing quality.

A method is provided for controlling a threshing system for an agricultural harvester. The method includes using a camera for obtaining images of a crop flow, processing the obtained images and controlling an operational setting of the threshing system. The images are obtained downstream of the threshing system, preferably somewhere between the threshing rotor or threshing drum and the residue spreader. The image processing aims at detecting grain ears in the obtained images, and to derive from those images at least one physical property of the detected grain ears. The operational setting of the threshing system are controlled in dependence of the derived at least one physical property.

A combine harvester including a grain elevator for transporting and elevating harvested grain. The grain elevator includes a paddle loop, a driver, an elevator housing, and a sensor assembly. The paddle loop has a plurality of paddles for carrying the harvested grain. The driver is arranged for driving the paddle loop and thereby moving the paddles upward at a first side of the paddle loop and downward at a second side of the paddle loop. The elevator housing encompasses at least a portion of the paddle loop. The sensor assembly comprises a NIR sensor for detecting properties of the harvested grain and a sensor window arranged for enabling infrared light to travel between the NIR sensor and the harvested grain. The sensor window is embedded in the elevator housing, adjacent the first side of the paddle loop.

A device for analyzing a grain sample including a light source, an image sensor, and a controller. The light source is configured for illuminating the grain sample. The image sensor is configured for capturing images of the grain sample. The controller is coupled to the image sensor and is configured for receiving the images of the grain sample therefrom and for analyzing the images to detect at least one material other than grain in the grain sample. The light source is configured for illuminating the grain sample with a local light spot having a size that is smaller than a width of an average wheat kernel. The image analysis and the detection of material other than grain may, at least partly, be performed using trained neural networks and other artificial intelligence algorithms.

An agricultural production machine comprising a characteristic diagram control is disclosed. The characteristic diagram control comprises one or more characteristic diagrams. Each characteristic diagram is configured to optimize operating parameters of the process units of the agricultural production machine. The particular characteristic diagram is designed as an initial characteristic diagram. In the initial characteristic diagram, at least the relationship between operating parameters of a process unit and quality parameters is described by initial operating points. A control characteristic curve is associated with the particular characteristic diagram, and the control characteristic curve lies around the minimum or maximum of the particular quality parameter.

A method of controlling a mobile agricultural machine that includes detecting a target value setting input identifying a target metric value for a quality metric representing a performance characteristic of the mobile agricultural machine and having an inverse relationship to machine speed, receiving machine data indicative of operating parameters on the mobile agricultural machine, generating, based on the machine data, a current metric value for the quality metric, determining a target machine speed based on the current metric value relative to the target metric value, and outputting a control instruction that controls the mobile agricultural machine based on the target machine speed.

A system is presented for evaluating grain in a grain handling system. The system includes a sensor system and a sample conveyor system. In one or more arrangements, the sensor system is operatively connected to the grain handling system and includes an optical sensor. The sample conveyor system is configured to collect samples of grain in the grain handling system, deliver the samples of grain to the sensor system, and remove the samples of grain from the sensor system. The sensor system is configured to measure characteristics of the samples of grain delivered to the sensor system by the sample conveyor system. In one or more arrangements, the system includes a cleaning system configured to clean the optical sensor, the lens; and/or other components of the sensor system. In one or more arrangements, the system includes a recalibration system configured to recalibrate the optical sensor.

A method for true-to-sample measurement by a mobile ingredient analysis system having a housing with a window, an interface for an external reference unit, a display and operating unit, a light source, an optical spectrometer, a camera, an internal reference unit, and an electronic control unit. The method includes: selecting a calibration product suitable for a sample to be examined; performing a plausibility check of the calibration product, an incorrect selection being signaled and an alternative calibration product being selected; outputting measurement conditions comprising the measurement point to be selected and measurement duration for the selected calibration product; capturing measured values of the sample by the spectrometer under the measurement conditions and with simultaneous monitoring of the measurement conditions; processing the captured measured values by means of an electronic control unit, each measured value captured while the measurement conditions were met being declared valid; outputting the measured values deemed valid.