In order to improve the execution efficiency of picking equipment, a six DOF robot arm, raspberry pie, single chip microcomputer, Android mobile terminal and server were used to build an intelligent fruit picking experimental system. It can automatically identify different kinds of fruits and realize automatic picking. Users can control the picking equipment and view the real-time picking video through the mobile terminal remotely. The solution process of inverse kinematics of manipulator in the three-dimensional coordinate system was realized by reducing the degree of freedom and using two-dimensional coordinate system, so as to avoid a lot of matrix operation and make the process of inverse kinematics solution of manipulator more simple. The Robotic Toolbox in Matlab was used to carry out 3D modeling and simulation of the manipulator, and the feasibility of dimension reduction was verified. In the fruit picking process, in order to make the trajectory of the manipulator more stable and coordinated, the pentanomic interpolation method was used to plan and control the trajectory of the manipulator. The object detection and pixel location of fruit was based on the YOLO v4 artificial intelligence recognition algorithm of Darknet deep learning framework. Using GPU training in the Ubuntu 19.10 operating system, totally 2000 images were used as a training set to train the recognition model of different kinds of fruits. The accuracy rate of each fruit recognition was more than 94%, the time for a single fruit picking about 17s. After the actual test, the system had good stability, real-time performance and the accuracy of fruit picking.