Ant Colony Optimization for UAV-based Intelligent Pesticide Irrigation System

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原文链接

NETL
IEEE

外文摘要：The application of unmanned aerial vehicle (UAV) to achieve precision irrigation in agriculture is a hot research topic in the industry。 However， much spay and much leakage of pesticide are tricky for the current UAV-based irrigation methods to deal with。 In this paper， we propose a new UAV-based irrigation system for precision agriculture。 First， considering that different areas in the same farmland may have different pesticide shortage， a map preprocess strategy is introduced to divide the entire farmland into pieces。 Second， we establish a UAV precision irrigation model and put forward an adaptive and fast dynamic ant colony optimization (AFD-ACO) algorithm to minimize the longest flight path with the lowest energy consumption and pesticide residues。 In order to promote the efficiency and the optimization effect， we utilize the scent pervasion rule to make the global map preprocessed and the neighborhood adaptive search policy to accomplish planning work。 Finally， comparing with other two ACO-based algorithms， the proposed algorithm is proved to be effective for the research problem， especially when the more pieces the farmland is divided， the better our solution performs。

外文关键词：

IrrigationAnt colony optimizationAdaptation modelsHeuristic algorithmsPower system stabilityUnmanned aerial vehiclesStability analysis

作者：

Zhikai Gao、Jie Zhu、Haiping Huang、Yifan Yang、Xudong Tan

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作者单位：

Nanjing University of Aeronautics and Astronautics, Nanjing, China

Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, China

Nanjing University of Posts and Telecommunications, Nanjing, China

会议名称：

International Conference on Computer Supported Cooperative Work in Design

会议地点：

Dalian(CN)

会议母体文献：

2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design

页码：

720-726

出版时间：

2021

DOI：

10.1109/CSCWD49262.2021.9437825