The shortest path detection for unmanned aerial vehicles via genetic algorithm on aerial imaging of agricultural lands

Abstract

By using unmanned aerial vehicles (UAV) for improving fertility of large agricultural lands in the GAP region, it is aimed to guide the end users through processing of the aerial images obtained by using image processing algorithms. The productivity problem of "Agriculture" sector that has the most important role in the economic development of the region directly has been solved in an innovative way by improving the fertility of agricultural lands. Related to the UAVs used for this process, the most important problem to consider is limited battery life. Therefore, it is very important to calculate the optimum route to reduce the flight time and to scan the large agricultural lands in the shortest time. In this paper, the shortest path problem is optimized by using the genetic algorithm for scanning large agricultural lands and collecting data. In the study, the points taken by UAV according to the field of view of the images are determined. The shortest path has been calculated by using genetic algorithm so that images can be taken from these determined points within a minimum flight time.

Keywords

• Genetic algorithm,Shortest path problem,Unmanned aerial vehicle

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