WHITE TEA BUD DETECTION BASED ON DEEP LEARNING RESEARCH

The quality of white tea buds is the basis of the quality of finished tea, and sorting white tea buds is a laborious, time-consuming, and key process in the tea-making process. For intelligent detection of white tea buds, this study established the YOLOv5+BiFPN model based on YOLOv5 by adding a Bidirectional Feature Pyramid Network (BiFPN) structure to the neck part. By comparing the YOLOv5 and YOLOv3 through the ablation experiment, it was found that the YOLOv5+BiFPN model could extract the fine features of white tea buds more effectively, and the detection average precision for one bud and one leaf was 98.7% and mAP@0.5 was 96.85%. This study provides a method and means for white tea bud detection based on deep learning image detection, and provides an efficient, accurate, and intelligent bud detection model for high-quality white tea sorting.