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.

Measurements of pea tenderness comparable to those provided by mechanical tenderometers are provided through analysis of an infrared spectrum of shelled peas. These measurements may be output directly or used to control real time harvesting of peas to prevent mixing of peas with different tenderness values.

A system and method for automated grain inspection and analysis of results during harvest, using an inspection system mounted on a combine harvester with geolocation tracking, allowing for real time analysis during harvest and tracking of grain quality by location of harvest.

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.

An electronic data processor is configured to identify the component pixels of a harvestable plant component within the obtained image data of plant pixels of the one or more target plants. An edge, boundary or outline of the component pixels is determined. The data processor is configured to detect a size of the harvestable plant component based on the determined edge, boundary or outline of the identified component pixels. A user interface is configured to provide an aggregate, sectional yield, or per row yield based on a detected size of the harvestable plant component for the one or more target plants as an indicator of yield of the one or more plants or standing crop in the field.

A sensing system and method is provided for crop material in a harvester. The sensing system includes a conveyor auger device oriented in a substantially vertical direction and having an entrance aperture, an exit aperture located upwardly above a radially oriented sensing aperture disposed therebetween. The conveyor auger device includes a conveyor auger having a flight for moving crop material upwardly, wherein the flight has a reduced radial extension adjacent the sensing aperture. A sensor disposed at or adjacent the sensing aperture senses the essentially continuously upwardly moving crop material for determining a property thereof.

A harvesting machine for cutting and processing crop, including a sensor arrangement, method for determining properties of crop when processing the crop with a harvesting machine, and method for determining properties of crop in a field.

One or more techniques and/or systems are disclosed for identifying traversability of a site. Site conditions of a target site affecting traversability can be identified using real-time or historical data. Soil conditions, weather conditions, moisture levels, and other factors may be used to determine whether a target site is traversable by a target piece of equipment. The traversability information can be identified for a target area, that comprises a target site, and a traversability map can be generated for the site. Target equipment specifications can be used to determine whether the target equipment may effectively traverse the target site based on the traversability map. Further, a traversability path may be generated for a piece of target equipment based on the traversability map, site conditions, and the equipment specifications. Additionally, site management may be enhanced using the site condition data to plan traversability of target equipment at the target site.

A method for measuring grain on a harvesting machine for harvesting cereals comprising: acquiring an image of the grain in the harvesting machine using a camera; and processing the image in an image processing system and generating an output value in respect of properties of the grain, wherein the output value contains information relating to at least one of the following quantities per unit area of the field: grain count, grain dimensions, and grain mass.

A grain tailings elevator for a combine harvester includes a an elevator housing having an interior containing a conveyor arrangement configured to transport grain tailings through the elevator housing to a discharge outlet. The elevator housing has a side wall with a window to the interior of the elevator housing. A camera having a camera housing containing an image sensor is mounted to the side wall of the elevator housing over the window with the image sensor in registration with the window. The image sensor is trained on the conveyor arrangement and configured to image the grain tailings transported by the conveyor arrangement through the elevator housing.