The grading of green jujube is a key factor in improving production efficiency and market competitiveness. However, traditional grading methods are inefficient, imprecise, and struggle to detect minor damages. This study proposes an improved BCW-YOLO deep learning model specifically designed for automated grading of green jujube. The model integrates a Bidirectional Feature Pyramid Network (BiFPN) and a Contextual Transformer Attention (COT) mechanism to enhance feature fusion accuracy and capture fine-grained details. In addition, the WIoU v3 loss function is introduced to optimize object localization performance. By constructing a multi-angle green jujube dataset and applying data augmentation techniques, the model’s generalization capability was significantly improved. The results of the experiement indicate that the improved BCW-YOLO achieves precision, recall, mAP, and F1 score of 90.87%, 92.12%, 95.66%, and 91.49%, respectively, representing increases of 1.93%, 2.77%, 1.58%, and 2.34% compared to the original YOLO model. Through comprehensive validation using confusion matrices, PR curves, heatmap analyses, and ablation studies, the model’s performance was thoroughly verified. Compared with other YOLO series models, BCW-YOLO performs exceptionally well in detecting minor damages, demonstrating its potential in practical agricultural grading. The findings provide a new technical approach for precise grading and automated sorting of green jujube, showing promising application prospects.
Chang et al. (Fri,) studied this question.