Critical regions identification and coverage using optimal drone flight path planning for precision agriculture

The use of drones in precision agriculture results in better resource management and effective decision making, particularly in the context of saving water resources and increasing crop yield. However, planning the optimal flight path for a drone to cover a given set of irrigation points is a challenging problem. In this paper, a novel method is proposed that combines image processing, data clustering, and heuristic algorithms to solve this problem. A drone-mounted camera is used to capture the multispectral image of a given land area. The proposed approach applies pre-processing steps to refine the collected image data. Contextual information is used to identify the areas of interest in the segmented image and a pool of these points is created. The K-means clustering algorithm is used to group points into clusters based on their proximity. For each cluster, the 2-opt heuristic algorithm is applied to find an approximate solution to the Traveling Salesman Problem (TSP), which minimizes the total distance traveled by the drone within the cluster. Then, the representative point of each cluster is found by computing the mean point and selecting the closest point to it. Finally, the 2-opt algorithm is applied again to connect the representative points of all clusters, thus forming a complete flight path for the drone. The method is evaluated on simulated and real-world data collected by the drone, and the result shows that the proposed approach produces efficient and feasible solutions for drone semantic localization and coverage. The experimental analysis shows 22% and 46% improvement over the basic approach without K-means clustering and a K-means no parallel approach.