Hyperspectral imaging for mushroom (agaricus bisporus) quality monitoring

Abstract

A method for mushroom quality grading based on hyperspectral image analysis in the wavelength range 400-1000 nm is presented. Different spectral and spatial pretreatments were investigated to reduce the effect of sample curvature on hyperspectral data. Algorithms based on chemometric techniques (Principal Component Analysis and Partial Least Squares Discriminant Analysis) and image processing methods (masking, thresholding, morphological operations) were developed for pixel classification in hyperspectral images.