Production Evaluation of Citrus Fruits based on the YOLOv5 compressed by Knowledge Distillation

Pre-harvest estimation of fruit production is crucial for fruit storage and price analysis in the planting of fruit trees. However, prior research consistently displays low accuracy because of problems with small objects, leaf occlusion, and fruit overlap, and they emphasize large networks that are unrealistic in the real world. In this study, we emphasize the use of smartphones to evaluate citrus fruit production. We suggest a simple method for detecting objections based on the YOLOv5 algorithm compressed by knowledge distillation. To extract the visual features, we first use mobilenetV2 as the foundation of YOLOv5. To learn the reliable detection features, we also incorporate an attention mechanism into YOLOv5. As such, we can obtain embedded Yolo served as a student model, which is learned via knowledge distillation. As such we can take the lightweight student model as the final detection model. Finally, we take the embedded Yolo to detect the citrus fruits and take a linear regression model to predict the number of counted fruits and the production is estimated. Experiments show that the proposed method can accurately count fruits and approximate the production.