Integrating multispectral reflectance and fluorescence imaging for defect detection on apples

Abstract

This research investigated multispectral imaging to detect various defects on apples. An integrated approach using multispectral imaging in reflectance and fluorescence modes was used to acquire images of three varieties of apples. Eighteen images from a combination of filters ranging from the visible region through the NIR region and from 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. Artificial neural network classification models were developed for two classification schemes, a two-class and a multiple-class. In the two-class scheme, pixels were categorized into normal or disordered tissue, whereas in the multiple-class scheme, pixels were categorized into normal, bitter pit, black rot, decay, soft scald, and superficial scald tissues. A 10-fold cross validation technique was used to assess the performance of the neural network models. The integrated imaging model of reflectance and fluorescence was effective on Honeycrisp variety, whereas single imaging models of reflectance or fluorescence was effective on Redcort and Red Delicious. The technique is promising for accurate recognition of different types of disorder on apple.