Hyperspectral imaging for mushroom (agaricus bisporus) quality monitoring

Author

Gowen, A.A. ; O´Donnell, C.P. ; Frias, J.M. ; Downey, G.

Author_Institution

Sch. of Agric., Univ. Coll. Dublin, Dublin, Ireland

fYear

2009

fDate

26-28 Aug. 2009

Firstpage

1

Lastpage

4

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.

Keywords

agriculture; image classification; least squares approximations; monitoring; principal component analysis; chemometric techniques; hyperspectral imaging; image processing; mushroom; partial least squares discriminant analysis; pixel classification; principal component analysis; quality monitoring; Calibration; Food technology; Hyperspectral imaging; Image analysis; Least squares methods; Monitoring; Pixel; Principal component analysis; Reflectivity; Testing; chemometrics; hyperspectral; imaging; mushrooms;

fLanguage

English

Publisher

ieee

Conference_Titel

Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009. WHISPERS '09. First Workshop on

Conference_Location

Grenoble

Print_ISBN

978-1-4244-4686-5

Electronic_ISBN

978-1-4244-4687-2

Type

conf

DOI

10.1109/WHISPERS.2009.5289074

Filename

5289074