The target spraying effect of spray robots mainly depends on the control performance of the spraying arm during the processes of aiming and tracking. To further improve the robustness of the endpoint control and positioning accuracy of the spray arm, an improved potential field algorithm for the motion planning and control of the spray arm is proposed based on prophase research. The algorithm introduces a velocity potential field, visual field constraints and joint position limit constraints into the traditional artificial potential field method. The velocity potential field is used to ensure that the target state of the spraying arm is at the same velocity as the target crop (relative velocity) to achieve stable target tracking. The visual field constraints and joint position limit constraints are utilized to ensure the efficiency of the visual servo control and the movement of the spray arm. The algorithm can plan a feasible trajectory for the spraying arm in Cartesian space and image space, and use the speed controller to control the spraying arm movement along the trajectory for aiming and tracking. Simulation analysis shows that the algorithm can plan better motion trajectories than the servo controller based on image moments in previous studies. In addition, the experimental results show that the algorithm can effectively improve the robustness of targeting and tracking control for the spray robot.