A Multi-Area Task Path-Planning Algorithm for Agricultural Drones Based on Improved Double Deep Q-Learning Net

Jian Li, Weijian Zhang, Junfeng Ren, Weilin Yu, Guowei Wang (), Peng Ding, Jiawei Wang and Xuen Zhang
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Jian Li: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Weijian Zhang: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Junfeng Ren: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Weilin Yu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Guowei Wang: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Peng Ding: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Jiawei Wang: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Xuen Zhang: College of Information Technology, Jilin Agricultural University, Changchun 130118, China

Agriculture, 2024, vol. 14, issue 8, 1-21

Abstract: With the global population growth and increasing food demand, the development of precision agriculture has become particularly critical. In precision agriculture, accurately identifying areas of nitrogen stress in crops and planning precise fertilization paths are crucial. However, traditional coverage path-planning (CPP) typically considers only single-area tasks and overlooks the multi-area tasks CPP. To address this problem, this study proposed a Regional Framework for Coverage Path-Planning for Precision Fertilization (RFCPPF) for crop protection UAVs in multi-area tasks. This framework includes three modules: nitrogen stress spatial distribution extraction, multi-area tasks environmental map construction, and coverage path-planning. Firstly, Sentinel-2 remote-sensing images are processed using the Google Earth Engine (GEE) platform, and the Green Normalized Difference Vegetation Index (GNDVI) is calculated to extract the spatial distribution of nitrogen stress. A multi-area tasks environmental map is constructed to guide multiple UAV agents. Subsequently, improvements based on the Double Deep Q Network (DDQN) are introduced, incorporating Long Short-Term Memory (LSTM) and dueling network structures. Additionally, a multi-objective reward function and a state and action selection strategy suitable for stress area plant protection operations are designed. Simulation experiments verify the superiority of the proposed method in reducing redundant paths and improving coverage efficiency. The proposed improved DDQN achieved an overall step count that is 60.71% of MLP-DDQN and 90.55% of Breadth-First Search–Boustrophedon Algorithm (BFS-BA). Additionally, the total repeated coverage rate was reduced by 7.06% compared to MLP-DDQN and by 8.82% compared to BFS-BA.

Keywords: precision agriculture; coverage path-planning; improved DDQN; LSTM; dueling network (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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