Detection of variety and wax bloom of Shaanxi plum during post-harvest handling

Abstract

The detection of plum variety and wax bloom has extensive applications in the fields of fruit classification and fruit quality assessment. By automating the detection and identification of plum varieties and wax bloom, it is possible to enhance the efficiency and accuracy of variety identification and quality assessment, and reduce manual intervention and misjudgment, thereby improving the market competitiveness of fruits. Currently, many works focus on improving the detection performance of single attribute detection of plum varieties or wax bloom, and it is often necessary to use two models to detect the same plum variety and quality information separately, which leads to inefficient and resource-consuming problems in practical applications. To solve this problem and improve the efficiency of detection, a Multi-Label detection model based on YOLOv7 is proposed. Firstly, the double detection head structure is introduced to improve the prediction ability for two types of attribute features. Then, the loss function suitable for multi-attribute labels is improved, and two classification loss functions are used to optimize the prediction results of the two types of attribute labels, respectively. Finally, a multi-label non-maximum suppression algorithm is proposed to solve the problem of filtering redundant bounding boxes of multi-attribute labels. Experimental results on the plum image dataset show that the proposed Multi-Label YOLOv7 model achieves a mAP@0.5 of 96.2 %, a precision of 94.6 %, and a recall of 89.5 %. The experimental results show that the Multi-Label YOLOv7 model can effectively detect variety and wax bloom attributes and improve the efficiency of multi-attribute label detection. The code and dataset for this experiment can be found at https://github.com/hejinrong/Muti-Label-YOLOv7.