Path Planning for Agricultural UAVs Based on Deep Reinforcement Learning and Energy Consumption Constraints
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/9/943 |
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| author | Haitao Fu Zheng Li Weijian Zhang Yuxuan Feng Li Zhu Yunze Long Jian Li |
| author_facet | Haitao Fu Zheng Li Weijian Zhang Yuxuan Feng Li Zhu Yunze Long Jian Li |
| author_sort | Haitao Fu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d7eae85fe98a438093d5d0f8dd574c74 |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-d7eae85fe98a438093d5d0f8dd574c742025-08-20T02:24:46ZengMDPI AGAgriculture2077-04722025-04-0115994310.3390/agriculture15090943Path Planning for Agricultural UAVs Based on Deep Reinforcement Learning and Energy Consumption ConstraintsHaitao Fu0Zheng Li1Weijian Zhang2Yuxuan Feng3Li Zhu4Yunze Long5Jian Li6College of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaTraditional 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.https://www.mdpi.com/2077-0472/15/9/943precision agriculturedeep reinforcement learning path planning Bi-RNN |
| spellingShingle | Haitao Fu Zheng Li Weijian Zhang Yuxuan Feng Li Zhu Yunze Long Jian Li Path Planning for Agricultural UAVs Based on Deep Reinforcement Learning and Energy Consumption Constraints Agriculture precision agriculture deep reinforcement learning path planning Bi-RNN |
| title | Path Planning for Agricultural UAVs Based on Deep Reinforcement Learning and Energy Consumption Constraints |
| title_full | Path Planning for Agricultural UAVs Based on Deep Reinforcement Learning and Energy Consumption Constraints |
| title_fullStr | Path Planning for Agricultural UAVs Based on Deep Reinforcement Learning and Energy Consumption Constraints |
| title_full_unstemmed | Path Planning for Agricultural UAVs Based on Deep Reinforcement Learning and Energy Consumption Constraints |
| title_short | Path Planning for Agricultural UAVs Based on Deep Reinforcement Learning and Energy Consumption Constraints |
| title_sort | path planning for agricultural uavs based on deep reinforcement learning and energy consumption constraints |
| topic | precision agriculture deep reinforcement learning path planning Bi-RNN |
| url | https://www.mdpi.com/2077-0472/15/9/943 |
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