Proceedings Article | 30 March 2004
KEYWORDS: Luminescence, Tissues, Reflectivity, Linear filtering, Image filtering, Optical filters, Visible radiation, Multispectral imaging, Ultraviolet radiation, Cameras
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-level classification
into normal or disorder tissue. Two classification schemes, a 2-class and a multiple class, were developed and tested in
this study. In the 2-class scheme, pixels were categorized into normal or disorder tissue, whereas in the multiple class
scheme, 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 scheme yielded highest accuracy with total accuracy of 95, 97, and
100 % for Honey Crisp, Red Cort, or Red Delicious respectively. In the multiple-class scheme, 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. Through variable selection analysis, in the 2-class scheme, fluorescence models yielded higher total
classification accuracy compared to reflection models. For Red Cort and Red Delicious, models with only FUV yield
more than 95% classification accuracy, demonstrating a potential of fluorescence to detect superficial scald. Several
important wavelengths, including 680, 740, 905 and 940 nm, were identified from the filter combination analysis. The
results indicate the potential of this technique to accurately recognize different types of disorder on apple.
