Selecting hyperspectral bands and extracting features with a custom shallow convolutional neural network to classify citrus peel defects
Quentin Frederick, Thomas F. Burks, Jonathan Adam Watson, Pappu Kumar Yadav, Jianwei Qin, Moon J. Kim, Mark A. Ritenour
Abstract
Citrus Black Spot (CBS) causes considerable damage to the Florida citrus industry. Early detection of CBS, especially in the presence of other peel blemishes, would enable better mapping and control of CBS spread, reduce wasted fruit, and permit early removal of culls from the packing stream. Oranges whose peels bore the symptoms of four defects/disease (CBS, greasy spot, melanose, and wind scar), as well as a normal control group, were imaged with a hyperspectral imaging system. Principal Component Analysis- (PCA) and Linear Discriminant Analysis (LDA) -based methods were employed to select bands from these images, and a custom convolutional neural network (CNN) for feature extraction was trained with these bands. The extracted features permitted classification of the peel conditions with four classifiers: SoftMax, Support Vector Machines (SVM), Random Forest Classifier (RFC), and K-Nearest Neighbors (KNN). A mean overall accuracy of 94.9% was achieved using an SVM classifier on five bands selected with PCA, and 90.2% with LDA-selected bands. These results show the potential of CNNs to extract features for automated postharvest citrus inspection.