Chinese Tea Sprout Dataset
On the basis of autonomous mobile tea picking robot, aiming at the shortcomings of traditional tea bud identification methods such as slow speed, low accuracy and poor adaptability, as well as people's demand for high-quality tea, the research and experiment of tea bud quality classification recognition based on YOLOv5 were carried out. Through the construction of the autonomous mobile tea picking robot visual recognition system, the data set was constructed, which mainly included tea image acquisition, enhancement and annotation. YOLOv5 and SSD target detection algorithms were used to conduct model training experiments, and the experimental data was analyzed. The experimental results show that the average accuracy of YOLOv5 target detection algorithm is high.The analysis of experimental data shows that the YOLOv5 target detection algorithm has a good effect on classification identification of tea buds, which can provide technical support and theoretical guidance for classification identification of tea buds and intelligent picking.
Divide the dataset into training sets, testing machines, and validation sets to use