Autonomous robotic weeding systems have demonstrated that they may realise their full potential in precision agriculture, where they are used to lessen the dependence that is currently placed on agrochemicals such as herbicides and insecticides, thereby reducing the amount of these substances that are used. pollution of the environment and the promotion of sustainable development both need to be addressed. On the other hand, the vast majority of the previous studies call for weed detection systems that are both rapid and able to function in order to offer real-time solutions. This rule out the possibility of implementing algorithms that are capable but need a longer amount of time to carry out, such as learning-based approaches. Using both mechanical weeding devices, we are currently in the process of presenting the conception, the actualization, and the testing of the roboion weed controller. In order to steer the machinery in such a way that high-precision weed removal can be carried out, a structure that is capable of carrying out naive Bayes filtering, as well as 3D direct intra- and inter-camera vision tracking, and predictive control, while simultaneously combining cutting-edge crop and weed detection algorithms is constructed. This allows for the machinery to be steered in such a way that high-precision weed removal can be carried out. This building was designed so that the machinery could be controlled. The results of the studies show that the fully operational weed control system that we have proposed is able to perform selective mechanical as well as chemical in-row weeding with indeterminate detection delays across a variety of topographical situations and phases of the crop’s development.