Abstract
The problem of detecting Fusarium fungi infected grains and identifying the varieties of the components of grain mixtures based on the combined use of various spectral measurement methods in various wavelength ranges is considered. A method for measuring combined reflectance and transmittance spectra of the elements of non-uniform grain flow is described. The results of the spectral measurement are processed using neural network based classification algorithms combined with data dimensionality reduction techniques. The probabilities of incorrect recognition for various numbers of features and combinations of spectral methods are estimated
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