Abstract
Smart farming is a popular agricultural concept that uses modern technology to optimize agricultural operations, increase efficiency, and enhance crop yields. Finding the most effective and ideal approach for different agricultural chores such as planting, irrigation, and harvesting using algorithms and embedded systems is one of the core issues in smart farming. In robotics, the A* algorithm is a popular pathfinding technique. To effectively discover the shortest route, the A* is well-suited for tackling the pathfinding issue in smart farming methods. Here, we focus our research on improving the well know A*. Specifically, we investigate how the A* algorithm might be modified to solve particular agricultural difficulties, such as travelling over unevenly shaped fields and avoiding obstacles. The research also looks at selecting appropriate heuristic functions for the A* algorithm, which may provide more efficient routes for various agricultural tasks. This paper demonstrates the effectiveness and benefits of using the proposed A* algorithm in smart farming applications through extensive simulations and real-world case studies. The results show significantly improved overall efficiency compared to the traditional A* algorithm.
