RESEARCH ON DEFECT IDENTIFICATION OF YU-LU-XIANG PEARS BASED ON IMPROVED LIGHTWEIGHT RESIDUAL NEURAL NETWORK MODEL

The skin of Yu-Lu-Xiang pears is brittle and easily damaged during picking and sorting. In order to reduce the secondary damage caused by mechanical automatic sorting of Yu-Lu-Xiang pear after harvest, optimize the sorting process and improve the sorting accuracy. Based on the MobileV2Net model, a lightweight convolutional neural network model EC-MobileV2Net-Fast, which integrated transfer learning and attention mechanism, was proposed to identify skin damage defects of Yu-Lu-Xiang pears. According to the defects of Yu-Lu-Xiang pears with different damage degrees, a dataset containing four characteristics was created. The model accuracy rate, single defect identification accuracy rate, recall, specificity, parameter and so on were taken as evaluation indexes, and the interpretation ability of the model decision was analyzed by Grad-CAM thermal map. Preliminary evaluation results showed that the model produced the highest level of accuracy, underscoring the potential of deep learning algorithms to significantly enhance defect recognition and classification. It can improve sorting efficiency, reduce labor costs and strictly control after-sales quality.