Green Plums Surface Defect Detection Based on Deep Learning Methods

Abstract

Green plums are a characteristic fruit resource in China, with a long history of cultivation. Many surface defects will appear in the growth, transportation and preservation of green plums which seriously affect the processing quality of by-products. The existing manual sorting method of green plums is limited by the experience of workers. It is difficult to ensure the quality and speed of detection. Therefore, the formation of automatic detection of green plums surface defects is of great significance to the development of green plum industry. According to the surface defects of green plums, this paper divides green plums into five categories: rot, cracks, scars, spots and normal. A total of 1235 images of green plums were obtained by self-built image acquisition device. The WideResNet50-AdamW-Wce model based on WideResNet model was built to classify the surface defects of green plums. Accuracy, recall and F1-measure were selected as the indexes to evaluate the accuracy of classification. The accuracy of classification reached 98.95 %, and the classification accuracy of rain spots, normal, scars, rot and crack reached 100 %, 99.56 %, 98.59 %, 98.25 % and 96.10 % respectively. Comparing the performance of ResNet50-SGD, WideResNet50-SGD, WideResNet50-SGD-Wce and WideResNet50-AdamW network models, the F1-Measure based on WideResNet50-AdamW-Wce is the highest in each defect, and more greengage defect features can be learned. The detection results can meet the production needs of plum deep processing enterprises – evaluating 1800 green plums per hour on the assembly line.