# The Application Status and Trends of Machine Vision in Tea Production
## 3 Intelligent Tea Picking
### Target Recognition and Localization  
The effective picking of tea buds cannot be achieved without precise positioning. Scholars have used machine vision technology to study the target recognition and positioning of tea buds. Target recognition is the process of separating tea buds from the background. It is a prerequisite for the robotic arm to automatically pick and complete the task. At the same time, calculations are performed between global, camera, and image coordinates to obtain the spatial coordinates of the target. Then, the robot's robotic arm moves to that position and carries out harvesting. The process of applying machine vision to the robot is shown in Figure 4.  
Figure 3: Different pest control devices: (**a**) pest monitoring system (PMS); (**b**) targeted pest control system (TPCS). Most of the detection of tea shoots in China is based on image processing methods, which use the differences in background information between tea shoots and old leaves, branches, and trunks to extract features such as color, shape, and texture for research. Based on this, a model is established for analysis, detection, and localization. [PERSON] et al. [33] conducted segmentation and detection experiments on tea buds on a single background, extracting the G component of the tea image in the RGB color space and using a dual threshold method to segment the image. Then, based on the shape characteristics of the tea buds, the edges of tea buds were detected, and the recognition accuracy for tea buds was 94%. [PERSON] et al. [34] transformed the original RGB image into an HIS (hue, lightness, saturation) color model and segmented it using the regional growth method by selecting H and S parameters. The accuracy was 94.6% at a shooting angle of 45\({}^{\circ}\), but the method was time-consuming. [PERSON] et al. [35], based on the lab color model and K-means clustering method, extracted the a and b components from the model for analysis, and achieved an average recognition rate of about 94%, with good preservation of leaf integrity. After 2015, with the rise of deep learning algorithms, [PERSON] [36] first applied the color3 algorithm, which had been improved through multiscale detection, to tea bud detection. Before conducting network training, image segmentation was performed on tea bud images under complex backgrounds by combining super-green features and the OSTU algorithm. It was found that the detection accuracy of tea bud images under complex backgrounds was high. [PERSON] et al. [37] proposed an improved YOLOv4 object detection model with the complex background of other tea leaves to predict the picking points of buds. The improved hybrid attention mechanism was introduced to make the tea bud detection model better adapted to the feature information of tea buds. [PERSON] et al. [38] solved the problem of missed detection caused by poor image contrast and weakened features under natural lighting by locally adapting gamma brightness based on the average grayscale value of the image. Experiments have shown that the model has strong robustness to light intensity. In the actual tea picking and production process, the problem of tender tea leaves being obstructed by leaves and the difficulty in separating tender buds from the background color have always been key technical difficulties. [PERSON] et al. [39] designed a segmented tea picking structure for tender tea bud image collection. By moving the image collection platform, the tender leaves of tea leaves are exposed from complex backgrounds, solving the problems of tea occlusion and difficulty in separating tender buds due to their similar color to the background. Then, the YOLOv4 network was used to detect tea buds, and the final recognition accuracy of tea buds reached 90%. However, currently, only image collection experiments have been conducted, but no in-depth research has been conducted on the coordinate positioning of picking points.