Publication:
Optimal Energy Consuming on Spraying an Agricultural Field by Using Multiple UAVs

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Date

2022

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Springer Science and Business Media Deutschland GmbH

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Abstract

Recently, agricultural areas are decreasing day by day in the face of the constantly increasing population. As a result, it is inevitable that existing production techniques are made much more efficient. In this study, starting from this point, it was aimed to spray the spraying areas of the pre-determined targets in the agricultural land of autonomous unmanned aerial vehicles in communication with each other with time minimization. For this purpose, two scenarios were compared on how to use the drones in the stations placed in all four corners of the field in the most effective way. In the first scenario, the field is divided into four equal parts in a classical way. In the second scenario, the field was divided into 2–4 regions by using the k-means method according to the areas to be sprayed. The route that the drone will use in spraying has been analyzed using the segmental method developed for the traveling salesman problem. For calculations, Julia programming language was used. Each scenario has been examined 100 times for different number of spraying sites. In light of the results obtained, it was found that the k-means method improved the flight time by an average of 19% compared to classical segmentation. In addition, with the developed method, unnecessary flight times of drones were prevented, and their useful lives were extended by finding which stations should be used the least in different situations. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Description

▪ Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 245).

Keywords

Autonomous Drones, K-means Method, Smart Agriculture, Traveling Salesman Heuristic

Citation

Güzey, A., Akıncı, M. M., & Mehmet Güzey, H. (2021, August). Optimal Energy Consuming on Spraying an Agricultural Field by Using Multiple UAVs. In Agriculture Digitalization and Organic Production: Proceedings of the First International Conference, ADOP 2021, St. Petersburg, Russia, June 7–9, 2021 (pp. 285-297). Singapore: Springer Nature Singapore.