Path Planning for Agricultural UAVs Based on Deep Reinforcement Learning and Energy Consumption Constraints

Haitao Fu, Zheng Li, Weijian Zhang, Yuxuan Feng, Li Zhu, Yunze Long and Jian Li ()
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Haitao Fu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Zheng Li: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Weijian Zhang: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Yuxuan Feng: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Li Zhu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Yunze Long: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Jian Li: College of Information Technology, Jilin Agricultural University, Changchun 130118, China

Agriculture, 2025, vol. 15, issue 9, 1-22

Abstract: Traditional pesticide application methods pose systemic threats to sustainable agriculture due to inefficient spraying practices and ecological contamination. Although agricultural drones demonstrate potential to address these challenges, they face critical limitations in energy-constrained complete coverage path planning for field operations. This study proposes a novel BiLG-D3QN algorithm by integrating deep reinforcement learning with Bi-LSTM and Bi-GRU architectures, specifically designed to optimize segmented coverage path planning under payload-dependent energy consumption constraints. The methodology encompasses four components: payload-energy consumption modeling, soybean cultivation area identification using Google Earth Engine-derived spatial distribution data, raster map construction, and enhanced segmented coverage path planning implementation. Through simulation experiments, the BiLG-D3QN algorithm demonstrated superior coverage efficiency, outperforming DDQN by 13.45%, D3QN by 12.27%, Dueling DQN by 14.62%, A-Star by 15.59%, and PPO by 22.15%. Additionally, the algorithm achieved an average redundancy rate of only 2.45%, which is significantly lower than that of DDQN (18.89%), D3QN (17.59%), Dueling DQN (17.59%), A-Star (21.54%), and PPO (25.12%). These results highlight the notable advantages of the BiLG-D3QN algorithm in addressing the challenges of pesticide spraying tasks in agricultural UAV applications.

Keywords: precision agriculture; deep reinforcement learning path planning Bi-RNN (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: 2025
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