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Design Autonomous Drone Control For Monitoring Tea Plantation Using Dynamic Programming and Kruskal Algorithm

机译:基于动态规划和Kruskal算法的监控茶园的自主无人机控制设计

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Indonesia is a country with the largest tea producers in the world, with a very large area needed tools to be able to help monitor the area of tea plantations as a whole. Unmanned Aerial Vehicle (UAV) wash chosen as a solution for the monitoring proses. Optimum flight path calculation is needed in order to produce good quality images, and also it influence to power consumption. The algorithm proposed in this study is Dynamic Programming and Kruskal Algorithm. Implementing these two network algorithms is expected to find the optimal path in aerial photography. The experimental results showed that the algorithm produced the optimum path, and more efficient power consumption than conventional lines. Image data obtained during tea plantation monitoring produced high-quality images, with the accuracy of each map above 90% and the assumption of errors below 5%.
机译:印度尼西亚是世界上最大的茶叶生产国,其所需的工具面积非常大,能够帮助监测整个茶园面积。选择无人飞行器(UAV)清洗作为监视程序的解决方案。为了产生高质量的图像,需要最佳的飞行路径计算,并且它也会影响功耗。本研究提出的算法是动态规划和Kruskal算法。期望实现这两种网络算法可以在航空摄影中找到最佳路径。实验结果表明,与常规线路相比,该算法产生了最优路径,并且功耗更低。在茶园监测过程中获得的图像数据产生了高质量的图像,每个地图的准确性均超过90%,误差的假设均低于5%。

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