The shortest path detection for unmanned aerial vehicles via genetic algorithm on aerial imaging of agricultural lands
Abdülkadir Gümüşçü, Mehmet Emin Tenekeci, Ahmet Tabanlıoğlu · DergiPark (Istanbul University) · 2018
By using unmanned aerial vehicles (UAV) for improving fertility of largeagricultural lands in the GAP region, it is aimed to guide the end users throughprocessing of the aerial images obtained by using image processing algorithms. Theproductivity problem of "Agriculture" sector that has the mostimportant role in the economic development of the region directly has been solvedin an innovative way by improving the fertility of agricultural lands. Relatedto the UAVs used for this process, the most important problem to consider islimited battery life. Therefore, it is very important to calculate the optimumroute to reduce the flight time and to scan the large agricultural lands in theshortest time. In this paper, the shortest path problem is optimized by using thegenetic algorithm for scanning large agricultural lands and collecting data. Inthe study, the points taken by UAV according to the field of view of the imagesare determined. The shortest path has been calculated by using genetic algorithmso that images can be taken from these determined points within a minimumflight time.