Dynamic optimization of stand structure in Pinus yunnanensis secondary forests based on deep reinforcement learning and structural prediction
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| Pubblicato in: | Frontiers in Plant Science vol. 16 (Oct 2025), p. 1610571-1610595 |
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| 001 | 3273797203 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1664-462X | ||
| 024 | 7 | |a 10.3389/fpls.2025.1610571 |2 doi | |
| 035 | |a 3273797203 | ||
| 045 | 2 | |b d20251001 |b d20251031 | |
| 100 | 1 | |a Zhao, Jian |u School of Mathematics and Computer Science, Dali University, Dali, China | |
| 245 | 1 | |a Dynamic optimization of stand structure in Pinus yunnanensis secondary forests based on deep reinforcement learning and structural prediction | |
| 260 | |b Frontiers Media SA |c Oct 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a IntroductionThe rational structure of forest stands plays a crucial role in maintaining ecosystem functions, enhancing community stability, and ensuring sustainable management. Although progress has been made in stand structure optimization, most existing studies focus on static improvements and fail to adequately capture the dynamic nature of stand development. In addition, commonly used heuristic and traditional methods often suffer from limitations in computational efficiency and generalization ability.MethodsTo address these challenges, this study explores the potential and advantages of multi-agent deep reinforcement learning in forest management, offering innovative insights and methods for achieving sustainable forest ecosystem management. Using the secondary forests of Pinus yunnanensis in southwest China as the research subject, we constructed an objective function and constraints based on spatial and non-spatial structure indexes. Selective harvesting and replanting were employed as optimization measures, and experiments were conducted on five circular plots to compare the performance of multi-agent deep reinforcement learning with that of multi-agent reinforcement learning. To account for the dynamic characteristics of stand structure, we further integrated structure prediction with multi-agent deep reinforcement learning for dynamic optimization across the five plots.ResultsThe results indicate that multi agent deep reinforcement learning consistently outperformed multi agent reinforcement learning across all plots. For the initial objective function values of each plot (0.3501, 0.3799, 0.3982, 0.3344, 0.4294), the optimized results obtained through multi agent deep reinforcement learning (0.5378, 0.5861, 0.5860, 0.5130, 0.6034) were significantly superior to the maximum objective function values achieved by multi agent reinforcement learning (0.5302, 0.5369, 0.5766, 0.5014, 0.5906). Furthermore, the dynamic optimization results incorporating structure prediction demonstrate that all plots progressively approached an ideal stand condition over multiple optimization cycles (0.5718, 0.6101, 0.6455, 0.5863, 0.6210), leading to a more balanced stand structure and improved long-term stability.DiscussionThis study proposes a novel stand structure optimization method that integrates multi agent deep reinforcement learning with structure prediction, providing theoretical support and practical guidance for the sustainable management of Pinus yunnanensis secondary forests. | |
| 651 | 4 | |a Yunnan China | |
| 651 | 4 | |a China | |
| 653 | |a Forest management | ||
| 653 | |a Stand structure | ||
| 653 | |a Collaboration | ||
| 653 | |a Deep learning | ||
| 653 | |a Sustainability management | ||
| 653 | |a Trends | ||
| 653 | |a Optimization | ||
| 653 | |a Biodiversity | ||
| 653 | |a Ecological function | ||
| 653 | |a Dynamic characteristics | ||
| 653 | |a Heuristic | ||
| 653 | |a Forests | ||
| 653 | |a Ecosystem management | ||
| 653 | |a Efficiency | ||
| 653 | |a Growth models | ||
| 653 | |a Pine trees | ||
| 653 | |a Sustainable forestry | ||
| 653 | |a Precipitation | ||
| 653 | |a Forest ecosystems | ||
| 653 | |a Predictions | ||
| 653 | |a Objective function | ||
| 653 | |a Trees | ||
| 653 | |a Terrestrial ecosystems | ||
| 653 | |a Learning | ||
| 653 | |a Multiagent systems | ||
| 653 | |a Stability | ||
| 653 | |a Algorithms | ||
| 653 | |a Economic | ||
| 653 | |a Pinus yunnanensis | ||
| 700 | 1 | |a Wang, Jianming |u School of Mathematics and Computer Science, Dali University, Dali, China | |
| 700 | 1 | |a Yin, Jiting |u Dali Forestry and Grassland Science Research Institute, Dali, China | |
| 700 | 1 | |a Chen, Yuling |u Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing, China | |
| 700 | 1 | |a Wu, Baoguo |u School of Information Science and Technology, Beijing Forestry University, Beijing, China | |
| 773 | 0 | |t Frontiers in Plant Science |g vol. 16 (Oct 2025), p. 1610571-1610595 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3273797203/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3273797203/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3273797203/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |