关键词: 冬枣, 高光谱成像, 特征波长, 轻微损伤, 检测

Abstract: Fruits of Chinese winter jujube (Zizyphus jujuba Mill. cv. Dongzao) are sensitive to mechanical stress and can easily develop brown spots after suffering mechanical stress during mechanical harvesting and postharvest handling. The damage cannot be detected easily by machine vision at very early stages of maturity. Thus, a near-infrared (NIR) hyperspectral imaging system was used to detect mechanical damage in Chinese winter jujubes. For reducing the dimensionality of hyperspectral data, three feature selection methods, successive projections algorithm, (SPA), correlation-based feature selection (CFS), and consistency, were used. In addition, three classifiers, i.e., k-nearest neighbor (k-NN), naive bayes (NB), and support vector machine (SVM), were evaluated to segment the pixels of the jujubes into two regions: damaged and nondamaged. Results revealed that two consistent wavebands, i.e., 1 353 nm and 1 691 nm, were established by all the feature selection methods. Besides, SVM offered the best performance with a correction recognition rate of 95.16% using the selected features by the consistency method. NB offered similar performance with a correction recognition rate of 84.26% in the selected wavebands. Hence, this work can pave the foundation for early on-line detecting Chinese winter jujube damage caused by mechanical stress.

Key words: Chinese jujube, hyperspectral imaging, feature selection, slight damage, detection