Design of Tea Impurity Detection System Based on YOLOv5 Deep Learning Algorithm

Tea has become an important beverage for entertainment and leisure in today's society, but the presence of impurities in tea can have adverse effects on the quality and taste of tea. Therefore, finding a method that can efficiently and accurately detect impurities in tea is of great significance. Deep learning technology has a wide range of applications in the field of image processing, especially with the rapid development of the YOLO (You Only Look Once) series of object detection algorithms in recent years. Enterprises can apply the YOLOv5 algorithm to a system for detecting tea impurities, and have conducted experimental evaluations on it. According to experimental data, the ratio of impurities detected by traditional impurity detection systems was between 13 %. 5-26%, while the ratio of impurities screened through YOLOv5 deep learning algorithm for tea impurity detection was reduced to between 2 % and 7%. 5%. In summary, it can be seen that the YOLOv5 deep learning algorithm had significantly improved the screening of impurities in tea. The use of this system can quickly and accurately detect impurities in tea, and is expected to play an important role in the practical application of tea production lines.