Autonomous robotic weeding systems in precision farming have demonstrated their full potential to alleviate the current dependency on herbicides or pesticides by introducing selective spraying or mechanical weed removal modules, thus reducing the environmental pollution and improving the sustainability. However, most previous works require fast weed detection system to achieve real-time treatment. In this paper , a novel computer vision based weeding control system is presented, where a non-overlapping multi-camera system is introduced to compensate the indeterminate classification delays, thus allowing for more complicated and advanced detection algorithms, e.g. deep learning based methods. The suitable tracking and control strategies are developed to achieve accurate and robust in-row weed treatment, and the performance of the proposed system is evaluated in different terrain conditions in the presence of various delays.