ASABE Technical Library - Abstract

Abstract. The automated early detection of fungal infection in citrus fruit is still a challenge for the citrus industry. In this study, the potential application of hyperspectral imaging was evaluated for automatic detection of the early symptoms of decay caused by Penicillium digitatum fungus in citrus fruit. Hyperspectral images of sound and decayed navel oranges were acquired in the wavelength range of 325–1100 nm. Principal component analysis (PCA) was applied to a dataset comprising of average spectra from decayed and sound tissue to reduce the dimensionality of data and observe the ability of Vis-NIR hyper-spectra to discriminate data from two classes. And, a mean normalization step is applied prior to PCA to reduce the effect of sample curvature on spectral profiles. Four wavelengths centered at 575, 698, 810 and 969 nm were selected as the characteristic wavelengths by analyzing the loadings of PC1. A combination image, which obtained by multiplying the characteristic loadings by corresponding to mean-normalized characteristic wavelength images of each orange sample, was obtained for identification of decayed fruits. Finally, a simple thresholding method was used to segment the decayed areas on fruit. The total success rate is 97.5% for 80 training samples and 93.8% for 80 test samples respectively, indicating that the proposed multispectral algorithm here is capable of detecting decay caused by penicillium digitatum in naval orange fruit.