Using Fluorescence Images in Classification of Apples

M. Codrea, E. Tyystjärvi (Finland), M. vandeVen, R. Valeke (Belgium), and O. Nevalainen (Finland)


Fluorescence image processing, neural network classifier, geometrical features.


Classification of harvested apples when predicting their storage quality is economically important. We describe a new automatic apple classification system based on chlorophyll a fluorescence images taken in blue light through a red filter. Fluorescence appears as a homogenous area broken by of small non-fluorescing spots, corresponding to normal corky tissue patches, lenticells, and to damaged areas that lower the quality of the apple. The classification relies on geometrical features of the spots and employs two neural networks. Experimental results show high potential of the technique. The system reached 96% accuracy in practical tests.

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