Integrating multispectral reflectance and fluorescence imaging for apple disorder classification.

摘要

Multispectral imaging in reflectance and fluorescence modes was used to classify various types of apple disorder from three apple varieties (Honeycrisp, Redcort, and Red Delicious). Eighteen images from a combination of filter sets ranging from the visible region through the NIR region and three different imaging modes (reflectance, visible light induced fluorescence, and UV induced fluorescence) were acquired for each apple as a basis for pixel-level classification into normal or disorder tissue. Two classification schemes, a 2-class and a multiple class, combined with four different classifiers, nearest neighbor, neural network, linear discriminant function and quadratic discriminant function, 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.;Total classification accuracy of the nearest neighbor classifier under the 2-class scheme for the full model, using all eighteen images, was 99.1, 96.8, 95.9, and 99.2% for Honeycrisp, Redcort, Red Delicious, and combined variety respectively. Furthermore, in the multiple-class scheme, the classification accuracy of Honeycrisp apple for normal, bitter pit, black rot, decay, and soft scald was 98.7, 99.3, 98.9, 98.5, and 100% respectively. These results indicate the potential of this technique to accurately recognize different types of disorder.;Performance result comparison of the four classifiers demonstrated that for Honeycrisp and combined variety, the nearest neighbor classifier yielded the highest accuracy followed by neural network, linear discriminant and quadratic discriminant classifiers. However, there were no significant differences among the classifiers on Redcort and Red Delicious.;Feature selection analysis to develop reduced-feature models was carried out through three different approaches, i.e. imaging mode combinations, filter combinations, and feature combinations. Imaging mode combinations analysis indicates a potential of integrating UV induced fluorescence and reflectance mode. Furthermore, the use of UV induced fluorescence alone has a potential to detect superficial scald in Red Delicious, and was able to classify black rot and soft scald on Honeycrisp with high accuracy, 100 and 99.4% respectively. Several important wavelengths were identified from the filter combination analysis, i.e. 680, 740, 905 nm. Reflectance at 680 nm relates to red color, and fluorescence response at 680 and 740 nm relates to the peaks of chlorophyll fluorescence emission, whereas the 905 NIR responses may relate to tissue physical characteristics. Feature combination analysis found the best 4-feature model resulted in total accuracy up to 96.6%, 98.8%, and 99.4% for Honeycrisp, Redcort, and Red Delicious respectively.