The development of on-line surface defect detection system for jujubes based on hyperspectral images

This paper presents the development of an on-line surface defect system using hyperspectral images for jujubes. A push-broom hyperspectral system was built for collecting hyperspectral image data and detecting skin defects of jujube online. Hyperspectral images with an effective wavelength range of 468–950 nm were obtained for jujubes with normal surface or common skin defect types (i.e., russeting, decay, white fungus, black fungus and crack). Support vector machine (SVM) and artificial neural networks (ANN) models were used to classify surface defects of jujubes. The classification accuracies, with the use of full wavelength range, of ANN and SVM models for jujube skin defects are 96.5% and 96.3% respectively. The times required for processing one jujube face are about 25 and 320 s for ANN and SVM models respectively. To reduce the computation time of online classification tasks, spectral bands were selected from a wavelength range of 468–760 nm with equal band intervals or by the principal component analysis (PCA) method. Experimental results showed that the accuracy of SVM and ANN models using 14 bands (469, 491, 513, 535, 558, 580, 602, 624, 646, 668, 691, 713, 735 and 757 nm), selected by equal wavelength intervals, were 94.4% and 95% respectively. And the accuracies of ANN and SVM models with 14 bands (470, 493, 534, 555, 590, 623, 632, 654, 672, 674, 683, 696, 707 and 747) selected by PCA are 95% and 94.6% respectively. The classification time, with the use of 14 bands, of ANN and SVM models for jujube skin defects reduced to 16.6 and 30 s respectively. The online line scanning and classification hyperspectral imaging system can be used for surface defect detection of other fruits.