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Integrating Reflectance and Fluorescence Imaging for Apple Disorder Classification

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

Citation:  Paper number  033120,  2003 ASAE Annual Meeting . (doi: 10.13031/2013.14064) @2003
Authors:   Diwan P. Ariana, Bim P. Shrestha, Daniel E. Guyer
Keywords:   Fluorescence, Reflectance, Multispectral, Imaging, Artificial neural network, Apples

Multispectral imaging in reflectance and fluorescence modes combined with neural network analysis was used to classify various types of apple disorder from three apple varieties (Honey Crisp, Red Cort, and Red Delicious). Eighteen images from a combination of filter sets and three different imaging modes (reflectance, visible light induced fluorescence, and UV induced fluorescence) were acquired for each apple sample as a base for pixel classification into normal or disorder tissue. Two classification models, a 2-class model and a 6-class model, were developed and tested in this study. In the 2-class model, pixels were categorized into normal or disorder tissue, whereas in the 6-class model, pixels were categorized into normal, bitter pit, black rot, decay, soft scald, and superficial scald tissues. Results indicate that single variety training under the 2-class model yielded highest accuracy with total accuracy of 95, 97, and 100 % for Honey Crisp, Red Cort, or Red Delicious respectively. In the 6-class model, the classification accuracy of Honey Crisp apple for normal, bitter pit, black rot, decay, and soft scald tissue was 94, 93, 97, 97, and 94 % respectively. The results indicate the potential of this technique to accurately recognize different types of disorder on apple.

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