An identification apparatus includes a plurality of irradiation units disposed at different positions in a conveyance width direction to irradiate a specimen with a converging ray in different irradiation conditions, the specimen being conveyed in a predetermined conveyance direction by a conveyance unit, a plurality of light-capturing units configured to capture scattered light from the specimen, each of the plurality of light-capturing units corresponding to a different one of the plurality of irradiation units, an acquisition unit configured to acquire identification information for identifying a property of the specimen, based on the light captured by the light-capturing units; and a placement unit configured to place the specimen on a position corresponding to any one of the plurality of irradiation units in accordance with a characteristic value of the specimen at an upstream side of the plurality of irradiation units in the conveyance direction.

A system, method, and apparatus for real-time characterization of drilled particles during a drilling operation can be comprised of a light illumination source to output short-wave-infrared (SWIR) light toward the drilled particles as the drilled particles exit a drill hole being drilled by a drilling machine; a sensor to sense reflected short-wave-infrared (SWIR) light reflected from the drilled particles exiting the drill hole; and processing circuitry operatively coupled to at least the sensor. The processing circuitry can be configured to determine a spectrum of the reflected short-wave-infrared light sensed by the sensor, and determine particle characterization for a portion of the drilled particles by performing hyperspectral analysis on the determined spectrum and based on predetermined candidate particle characterizations.

The present invention relates to a method of identifying and/or distinguishing materials by means of luminescence, wherein at least one luminescent substance is incorporated into the material and/or applied onto the material and the luminescence behaviour of the substance is analysed after excitation by means of radiation, and the use thereof for identifying and/or sorting and/or recycling and/or authenticating and/or performing a quality check and/or formulation check on materials.

A device and method for reliably and accurately detecting impurities in a bulk material comprising two opposing tunnel sections arranged such that a bulk material stream flows between or through the tunnel sections. At least one of the tunnel sections has a lighting means configured for indirectly illuminating the bulk material stream. Furthermore, an optical detector receives the light emitted from the illuminated bulk material. The lighting means and optical detector are configured about the tunnel sections such that the optical radiation optical radiation does not pass directly from the lighting means to the bulk material, nor from the bulk material stream to the optical detector An evaluation apparatus, responsive to measured data from the optical detector, identifies impurities in the bulk material. The invention moreover relates to a method for operating such a device.

Procedure for the detection of bitter almonds based on the processing of digital images, and a system and device associated therewith. Detection procedure and system for the automated classification of sweet and bitter almonds based on the processing of digital images. The fluorescence of the cyanogenic compounds naturally present in almonds generates a clear difference in colour between sweet and bitter almonds which subsequently is analysed and classified by means of a computer program. The invention also includes the device, either portable or automatic, for carrying out the classification of bitter or sweet almonds. This device will be necessary during the goods reception process and in the validation/verification of the quality of the finished product, prior to the loading and transport process.

Described herein is a spectrometer device for optical analysis of at least one sample. The spectrometer device includes: at least one housing having at least one entrance window; at least one wavelength-selective element configured for separating incident light into a spectrum of constituent wavelengths, the wavelength-selective element being disposed within the housing; at least one detector device configured for detecting at least a portion of the constituent wavelengths, the detector device being disposed within the housing; and at least one contact sensor device for detecting a contact of the spectrometer device with the sample, where the contact sensor device includes at least one optical contact sensor device, where the optical contact sensor device is configured to detect an influence of the presence of the sample onto the transmission of the optical signal.

An agricultural harvesting machine, with at least one work assembly and a monitoring assembly, is disclosed. The agricultural harvesting machine transports harvested material in a harvested material flow along a harvested material transport path. The monitoring assembly includes an optical measuring system positioned on the harvested material transport path and an evaluation device configured to determine at least one harvested material parameter. The optical measuring system includes a first optical sensor that senses spatially-resolved image data indicative of visible light in a first section and second optical sensor that senses image data indicative of invisible light in a second section that at least partly overlaps the first section. The evaluation device correlates the image data for the overlapping section from the first optical sensor and from the second optical sensor and determines, based on the correlation, at least one harvested material parameter.

In a grain sampling and imaging device, a grain bin (1) is located under a sampling body (5), a grain feed connector (4) is fixed to a top of the sampling body (5), an observation window (2) is installed on the sampling body (5), a camera (3) is installed above the observation window (2); grains fall to a grain feed connector (4), and then into the sampling body (5), and then are sieved by multiple passages of the sampling body (5), a part of the grains randomly enter the observation window (2) and photographed by the camera (3), and finally all of the grains enter the grain bin (1) through a discharge outlet (5.1) provided at a bottom of the sampling body (5).

Provided herein is a method for measuring the size distribution and/or hardness of free falling rock pieces. The method comprises projecting at least one laser line on the falling rock pieces by a laser device; capturing images of the falling rock pieces at an angle from the at least one laser line by at least one camera; and obtaining size distribution data of the falling rock pieces based on data obtained from a topographical map generated from the captured images. Certain embodiments further comprise: obtaining at least one of the volume and area of individual rock pieces from the topographical map; conducting a data analysis on at least one of the volume and area measurements of the rock pieces to reduce at least one of sampling and measurement errors; determining the size distribution of the falling rock pieces based on the data analysis and, optionally, evaluating a rock hardness index for the rock. Further provided is a method comprising: producing two topographical maps of the pieces from captured images; and obtaining the volume of pieces from the topographical map by adding half-volumes from each of the topographical maps.

A method and system for controlling the quality of harvested grains include capturing, by one or more image sensors, one or more images of material at a sampling location within a grain elevator of the combine harvester. The captured images are defined by a set of image pixels represented by image data and having a classification feature indicative of grain or non-grain material. One or more controllers receive the image data associated with the one or more images captured by the image sensor(s) and select a sample image defined by a subset of image pixels of the set of image pixels. The controller(s) apply a convolutional neural network (CNN) algorithm to the image data of the subset of image pixels of the selected sample image to determine the classification feature. The controller(s) analyze the determined classification feature to adjust an operational parameter of the combine harvester.