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DETECTION OF DEFECTS ON SELECTED APPLE CULTIVARS USING HYPERSPECTRAL AND MULTISPECTRAL IMAGE ANALYSIS

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Applied Engineering in Agriculture. Vol. 18(2): 219–226 . (doi: 10.13031/2013.7790) @2002
Authors:   P. M. Mehl, K. Chao, M. Kim, Y. R. Chen
Keywords:   Food safety, Fruit, Machine vision, Spectroscopy

Apple defects cause food safety concerns touching the general public and strongly affect the commodity market. Because accumulations of human pathogens are usually observed on surface lesions, detection of lesions is essential for assuring quality and safety. This article presents the application of hyperspectral image analysis to the development of multispectral techniques for the detection of defects on three apple cultivars: Golden Delicious, Red Delicious, and Gala. Two steps were performed: (1) hyperspectral image analysis to characterize spectral features of apples for the specific selection of filters to design the multispectral imaging system and (2) multispectral imaging for rapid detection of apple contaminations. Good isolation of scabs, fungal, soil contaminations, and bruises was observed with hyperspectral imaging using either principal component analysis or the chlorophyll absorption peak. This hyperspectral analysis allowed the determination of three spectral bands capable of separating normal from contaminated apples. These spectral bands were implemented in a multispectral imaging system with specific band pass filters to detect apple contaminations. In this preliminary work with 153 samples, good separation between normal and contaminated apples was obtained for Gala (95%) and Golden Delicious (85%). However, separations were limited for Red Delicious (76%).

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