As a non-destructive, real-time, fast, objective, and economical detection method, machine vision technology has been widely used in target recognition and positioning in various fields in recent years. Selective picking of tea buds is an important prerequisite for high-quality and efficient tea production. Autonomous picking of tea based on machine vision has become a research hotspot at home and abroad. At present, many scholars have studied the visual recognition and positioning methods in the process of intelligent tea picking, but the effect has not yet reached the actual picking requirements. In order to give everyone a comprehensive understanding of the existing research methods, this article systematically reviews the tea bud segmentation and bud picking point positioning methods based on machine vision technology, and prospects the application prospects and directions of machine vision technology in tea picking.

Aiming at the problem of poor grasping performance and low repetitive teaching efficiency when traditional machine vision-based robots are facing the grasping tasks of objects with unknown heights, an adaptive grasping method of robotic manipulator based on depth visual perception feedback control is proposed. The RGB-D image is acquired through the depth camera, and the embedded computing device is used to process the RGB-D image based on ROS(Robot Operating System) to obtain the object positioning information. Finally, the coordinate information of the object is used to control the joint motion of the robot to perform the grasping task. The experimental results show that the method is robust and requires a small amount of data, and it can grasp the target object with unknown height without teaching.