Detection of common defects on oranges using hyperspectral reflectance imaging

To detect various common defects on oranges, a hyperspectral imaging system has been built for acquiring reflectance images from orange samples in the spectral region between 400 and 1000 nm. Oranges with insect damage, wind scarring, thrips scarring, scale infestation, canker spot, copper burn, phytotoxicity, heterochromatic stripe, and normal surface were studied. Hyperspectral images of samples were evaluated using principal component analysis (PCA) with the goal of selecting several wavelengths that could potentially be used in an in-line multispectral imaging system. The third principal component images using six wavelengths (630, 691, 769, 786, 810 and 875 nm) in the visible spectral (VIS) and near-infrared (NIR) regions, or the second principal component images using two wavelengths (691 and 769 nm) in VIS region gave better identification results under investigation. However, the stem-ends were easily confused with defective areas. In order to solve this problem, representative regions of interest (ROIs) reflectance spectra of samples with different types of skin conditions were visually analyzed. The researches revealed that a two-band ratio (R875/R691) image could be used to differentiate stem-ends from defects effectively. Finally, the detection algorithm of defects was developed based on PCA and band ratio coupled with a simple thresholding method. For the investigated independent test samples, accuracies of 91.5% and 93.7% with no false positives were achieved for both sets of selected wavelengths using proposed method, respectively. The disadvantage of this algorithm is that it could not discriminate between different types of defects.