The present disclosure generally relates to methods and apparatuses that determine quality and authenticity (e.g., adulteration, incorrect labeling, etc.) of agricultural commodities based on near-infrared spectrometers and chemometrics.

The present disclosure relates to bioreactor tiles including fluidic channels and optical waveguides. One example system includes a substrate having a first channel, a second channel, and a third channel defined therein. The three channels are separated from one another by partial wall structures. The system also includes an optical waveguide configured to receive illumination light at a first end of the optical waveguide; propagate the illumination light toward a second end of the optical waveguide; allow at least a portion of the illumination light to escape the optical waveguide from a first surface of the optical waveguide as the illumination light propagates toward the second end of the optical waveguide; and provide the portion of the illumination light that escapes the optical waveguide from the first surface to the second channel or the third channel.

System and method of detecting infestation in agricultural products is disclosed. In one embodiment, an infestation detection system captures hyperspectral imaging data and depth imaging data for a plurality of points on an agricultural product upon directing a light source at the agricultural product. The system analyses the captured imaging data to derive morphological details as well as spectral signatures for complete 360° view of the plurality of points on the agricultural product. In an embodiment, the spectral signatures may be corrected for one or more pixels associated with the plurality of points in the agricultural product by integrating the hyperspectral imaging data with the depth imaging data. The system further classifies one or more regions of the agricultural product based on matching of spectral signatures for the one or more pixels with pre-defined spectral signatures stored for a plurality of agricultural products, and accordingly detect infestation.

Systems and methods are disclosed for determining a ripeness, firmness, or consumption suitability for food items, such as produce and fruit. The disclosure can provide for generating a machine learning model to detect food item ripeness. The model can be generated using destructive and non-destructive measurements of one or more food items. The model can then be applied to spectral imaging data of food items in real-time. The spectral imaging data can be captured by a point spectrometer. Using the model and spectral imaging data, the ripeness of the food items can be determined in a non-destructive manner. The determined ripeness of the food items can then be used to determine one or more supply chain modifications.

Provided is a method for monitoring a manufacturing process of an agricultural product. The method utilizes hyperspectral imaging and comprises scanning at least one region along a sample of agricultural product using at least one light source of a single or different wavelengths; generating hyperspectral images from the at least one region; determining a spectral fingerprint for the sample of agricultural product from the hyperspectral images; and comparing the spectral fingerprint so obtained to a spectral fingerprint database containing a plurality of fingerprints obtained at various points of the manufacturing process, using a computer processor, to determine which point in the manufacturing process the sample has progressed to.

Provided is a method for blending of agricultural product utilizing hyperspectral imaging. At least one region along a sample of agricultural product is scanned using at least one light source of different wavelengths. Hyperspectral images are generated from the at least one region. A spectral fingerprint for the sample of agricultural product is formed from the hyperspectral images. A plurality of samples of agricultural product is blended based on the spectral fingerprints of the samples according to parameters determined by executing a blending algorithm.

The invention relates to a device for crumbling root crops into substantially equal sized pieces, comprising: a main frame having an inlet side and an outlet side; a root crop supply at the inlet side; at least one crumbling shaft rotatable supported in the main frame, the crumbling shaft being provided with a plurality of curved hooks, which are curved into a direction of rotation of the crumbling shaft; and a non-rotating cutting rake having a plurality of protrusions and recesses and forming a counter blade for the hooks, wherein the hooks are arranged for interlaced movement with said recesses of the non-rotating rake. The invention moreover concerns a system and a corresponding method.

The disclosure discloses a method for near-infrared spectral wavelength selection based on an improved team progress algorithm (iTPA), belonging to the field of near-infrared spectral detection. The method includes: equally dividing near-infrared spectral wavebands to be selected into elite groups, ordinary groups and garbage collection groups according to evaluation values from high to low; generating a new waveband in the elite group or the ordinary group, where a wavelength point of the new waveband is selected from a random waveband in the selected group, and the wavelength point of the new waveband inherits the selected wavelength point; and enabling the inherited new waveband to select a learning behavior or an exploration behavior according to a set probability to update the wavelength point of the new waveband to generate a candidate waveband; and selecting a waveband with a highest evaluation value in the elite group as the waveband to be selected. Under the condition of ensuring the model prediction accuracy, the disclosure greatly reduces the number of wavelength variables, reduces the complexity of the algorithm at the same time, and improves the non-destructive detection accuracy in crops.

A method and a device for crop canopy chlorophyll fluorescence three-dimensional distribution information acquisition are provided. The device includes a Cropobserver canopy chlorophyll fluorescence detection device, a 3D camera and a computer system. The 3D camera is connected to the computer system, Visual studio 2017 and MATLAB 2018 are run in the computer system, and the Visual studio 2017 calls a point cloud library and a computer vision library to realize three-dimensional visualization of chlorophyll fluorescence information of crops to be tested. By means of the new method and the new device, the problem of incompleteness of the two-dimensional chlorophyll fluorescence information distribution acquired is solved, overall 3D visual distribution of crop canopy chlorophyll fluorescence distribution is realized, and important technical support is provided for acquisition and research of three-dimensional visual distribution information of chlorophyll fluorescence of the whole crop canopy.

The invention relates to a system and method for determining a status of plants. The system includes a light transmitter arranged for providing broad band illumination to one or more plants. The system includes a light receiver including a plurality of receiver channels, the receiver channels arranged for receiving light from said one or more plants in mutually different wavelength bands. The system includes a processing unit arranged for determining a status of said one or more plants on the basis of light received by the plurality of receiver channels. The transmitter may be arranged for transmitting bursts of modulated light.