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.

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.

A method for determining a quality condition of an agricultural product comprises: receiving a received light at a light detector, the received light comprising reflected, scattered, refracted, and/or deflected light from the agricultural product; transmitting the received light to a spectrometer; producing agricultural product (AP) spectral data of the received light using the spectrometer; with a computer in electrical communication with the spectrometer, comparing the AP spectral data to reference spectral data to determine whether the agricultural product has the quality condition, the reference spectral data corresponding to known quality conditions of the agricultural product; and with the computer, generating an output signal corresponding to the quality condition of the agricultural product.

ultraviolet (UV) based imaging method for determining protein concentrations of unknown protein samples based on automated multi-wavelength calibration. In various embodiments, a processor receives each of a standard set of wavelength data and an unknown set of wavelength data as recorded by a detector. Each standard set of wavelength data and unknown set of wavelength data defines a series of absorbance-to-wavelength value pairs across a first range of wavelengths selected from a range of a single-wavelength light beams of a UV spectra. The processor generates a multi-wavelength calibration model based on each of a first series of first absorbance-to-wavelength value pairs of the standard set of wavelength data. The processor implements the multi-wavelength calibration model to determine, for each unknown protein sample of the given unknown protein samples, a plurality of protein concentration values.

The disclosed embodiments include thin film multivariate optical element and detector combinations, thin film optical detectors, and downhole optical computing systems. In one embodiment, a thin film multivariate optical element and detector combination includes at least one layer of multivariate optical element having patterns that manipulate at least one spectrum of optical signals. The thin film multivariate optical element and detector combination also includes at least one layer of detector film that converts optical signals into electrical signals. The thin film optical detector further includes a substrate. The at least one layer of multivariate optical element and the at least one layer of detector film are deposited on the substrate.

The present disclosure relates to a spectrometer using multiple light sources. The spectrometer includes: a sample unit accommodating the sample; a multiple-light-sources unit irradiating light of different wavelengths to the sample unit; a sensor unit configured to measure absorbance generated at a wavelength of a light source irradiated to the sample unit; and a multiple scatterer configured to amplify the number of multiple scattering of the light source irradiated to the sample unit, wherein the sensor unit derives spectrum information by measuring absorbance at different wavelengths.

The conformal spectral library training method (CSLTM) of the disclosure allows sets of voltages for an optical filter to be calculated by way of a direct calculation without processing large amounts of spectral information, which significantly increases the speed of processing spectral information.

An object recognition apparatus includes a first spectrometer configured to obtain a first type of spectrum data from light scattered, emitted, or reflected from an object; a second spectrometer configured to obtain a second type of spectrum data from the light scattered, emitted, or reflected from the object, the second type of spectrum data being different from the first type of spectrum data; an image sensor configured to obtain image data of the object; and a processor configured to identify the object using data obtained from at least two from among the first spectrometer, the second spectrometer, and the image sensor and using at least two pattern recognition algorithms.

Aspects of the disclosure relate to techniques for analyzing unknown sample compositions using a prediction model based on optical emission spectra. One method comprises: receiving first emission spectra corresponding to a training sample comprising a plurality of pure elements of known concentrations; determining, based on the first emission spectra, a plurality of spectral regions corresponding to the plurality of pure elements of known concentrations; determining, for each spectral region corresponding to each pure element of a known concentration, features associated with a signature peak of the spectral region; training a prediction model to predict unknown concentrations of a plurality of constituents of an unknown sample based on an emission spectra of the unknown sample; receiving second emission spectra corresponding to the unknown sample comprising a plurality of constituents of unknown concentrations; and generating, based on the application of the trained prediction model, a concentration for each of the constituents of the unknown sample.

A camera system includes one or more spectral illuminators, a tunable optical filter, and a sensor array. Active spectral light emitted from the one or more spectral illuminators towards a scene is dynamically tuned to an illumination sub-band selected from a plurality of different illumination sub-bands. Sequentially for each of a plurality of fluorescing light sub-bands different than the selected illumination sub-band, the tunable optical filter is adjusted to block light from being transmitted from the scene to the sensor array in all but a tested fluorescing light sub-band from the plurality of different fluorescing light sub-bands, and the sensor array is addressed to acquire one or more image of the scene in the tested fluorescing light sub-band.