Detection of common defects on mandarins by using visible and near infrared hyperspectral imaging

Abstract

The presence of surface defects is one of the most influential factors in the quality and price of fresh fruit because consumers usually associate quality with a good appearance and without skin defects. Therefore, one of the main purposes of automatic detection of fruit quality is to differentiate between defective ones from sound fruits. However, the detection of defective fruits has always been a challenging task, especially the simultaneous detection of multiple types of defects. This work focuses on the development of multispectral image classification algorithm for detecting the common defects on mandarins based on the visible-near infrared (Vis-NIR) hyperspectral imaging technique. ‘Nanfeng’ mandarins with sound peel and four types of defects (i.e., anthracnose, scarring, decay and thrips scarring) were studied. Principal component analysis (PCA) was used to reduce hyperspectral data dimensionality with the goal of selecting several wavelengths that could be potentially used in an in-line multispectral imaging system. Two characteristic wavelength images at 680 nm and 715 nm in the visible spectral region were selected, and then the second principal component image (PC-2) and ratio image (Q680/715) based on these two characteristic wavelengths were used for defect detection and stem-end identification, respectively. Finally, the detection algorithm of defects was developed based on PC-2 image and ratio image (Q680/715) coupled with a simple thresholding method. For the investigated 356 independent test samples, classification accuracy of 96.63% indicated that the proposed multispectral image algorithm was effective for distinguishing between sound and defective ‘Nanfeng’ mandarins. Only two wavelength images were used in the algorithm, which was very helpful to develop a fast multispectral imaging system for on-line grading of mandarins.