A novel heuristic programming-based intelligent controller for autonomous farming

Abstract

Purpose This paper aims to develop one heuristic programming approach-based intelligent controller with unique necessary conditions for autonomous farming in a crop field, which improves path navigation and fewer damages to the vegetable crops in given environments. The proposed controller has also used the object detection and pesticide spraying technique with a ground robotic vehicle (GRv). Design/methodology/approach The controller contains a unique cost function, which approximates the cost of the easiest solution for the entire operation. Every time the main loop of the controller iterates, it detects the best-estimated solution from the given values. One path planning strategy is implemented in a crop field with the GRv in the simulated and experimental environment. The GRv moves to different planned positions of the crop field by avoiding obstacles and also optimizes the travelled path. Findings The proposed controller is also compared with the existing approaches to validate its efficacy. In comparison to the modified DAYANI and the advanced sine-cosine algorithm-advanced ant colony optimization strategy, the heuristic programming approach-based intelligent controller has convincingly improved the length of the navigational path by 4.53% and 3.7%, respectively. Originality/value In real time, the highest speed achieved by the GRv in the crop field is approximately 1.5 km/h. The proposed controller performs successfully in the given environments.