WHITE TEA BUD DETECTION BASED ON DEEP LEARNING RESEARCH

Abstract

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.