Detecting Citrus Canker by Hyperspectral Reflectance Imaging and Spectral Information Divergence

Abstract

Citrus canker is one of the most devastating diseases that threaten marketability of citrus crops. This research was aimed to develop a hyperspectral imaging approach for detecting canker lesions on citrus fruit. A hyperspectral imaging system was developed for acquiring reflectance images from citrus samples in the spectral region from 450 nm to 930 nm. Ruby Red grapefruits with cankerous, normal and other common peel diseases including greasy spot, insect damage, melanose, scab, and wind scar were tested. Spectral information divergence (SID) classification method, which was based on quantifying the spectral similarities by using a predetermined canker reference spectrum, was performed to the hyperspectral images of the grapefruits for differentiating canker from normal fruit peels and other citrus diseases. The overall accuracy for canker detection was achieved as 96.2% using an optimized SID threshold value of 0.008. This research demonstrated that hyperspectral imaging technique coupled with the SID based image classification method could be used for discriminating citrus canker from other confounding diseases.