# The Application Status and Trends of Machine Vision in Tea Production
## 3 Intelligent Tea Picking
### Target Recognition and Localization
The identification of tea buds is a prerequisite for achieving intelligent picking, and the extraction of picking position information through the identified positioning is also a  
Figure 4: Application of machine vision in tea harvesting. key production technology point for achieving intelligence, precision, and efficiency. When collecting images in the natural environment of the field, it is inevitable that the bud tip is covered by branches and leaves, disturbed by wind, and mechanically vibrated. At the same time, natural light can also have a significant impact on the image, as shown in Figure 5, which poses great challenges for target positioning. In recent years, domestic and international scholars have proposed many effective methods for the research of 3D positioning based on machine vision, among which widely used methods include 3D positioning based on monocular color camera, stereo vision matching, depth camera, laser rangefinder, time of flight (TOF) of optical 3D camera, etc. In the research on tea picking positioning, the current mainstream choice is to first process RGB images, analyze and obtain two-dimensional positions through deep learning networks, and then combine the depth image comparisons to perform three-dimensional positioning of picking points. The first-order deep learning object detection method represented by YOLO and SSD exhibits high speed and can avoid false positives caused by background errors. After learning the advantages of generalization features of objects, [PERSON] [40] extracted tea bud images using the PSO-SVM algorithm, and then used the yolov3-416 model to locate picking points and determine two-dimensional coordinates. Finally, binocular ranging was used to convert three-dimensional coordinates, with a segmentation accuracy of over 94% and an average time of about 1 s. [PERSON] et al. [41] used the improved YOLOV4 algorithm for coarse extraction of the foreground, and then used RGB HSV color conversion to obtain the contour of the extracted bud leaf area. Based on morphological algorithms, the picking point was located. [PERSON] [42] identified the bud leaf area in RGB images using the YOLOv4 bud leaf recognition model, and extracted the main contour of the bud leaf using the H-S manual threshold image segmentation method in the HSV color space. Using the \"shoot corrosion method\" to locate the two-dimensional pixel coordinates of the picking points in the main contour, they were able to bring the two-dimensional pixel coordinates of the picking points into the depth image to obtain the depth distance data of the picking points. Based on the internal parameters calibrated by the depth camera and the camera imaging matrix, they could calculate and obtain the three-dimensional positioning of the bud and leaf picking points. Three different methods were compared in the article, as shown in Table 2.  
The second-order deep learning object detection method represented by R-CNN is an excellent deep learning algorithm with high accuracy (positioning, detection rate) and advantages such as shared computing power. [PERSON] et al. [43] used an improved Fast R-CNN to extract the foreground, then used FCN-16 for image segmentation of the picking area, and finally used the centroid algorithm to obtain the picking points. This method has high detection accuracy but does not handle occlusion and other situations well. [PERSON] et al. [44] used Mask RCNN as the model to establish a tea bud, tea leaves, and tea picking point recognition model. It added a semantic segmentation function to the faster RCNN and directly segmented the picking point area. The overall accuracy and recall  
Figure 5: Tea bud images with different light regimes: (a) backlight; (b) direct sunlight; (c) occlusion. rates were 93.95% and 92.48%. However, the running efficiency of the model still needs to be improved, and there are still some errors in determining the selected area. The optimal effect can only be achieved at a specific angle. [PERSON] et al.