Dynamic optimization of stand structure in Pinus yunnanensis secondary forests based on deep reinforcement learning and structural prediction
This 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 <i>Pinus yunnanensis</i> secondary forests.
Authors
- Jianming Wang (ORCID: https://orcid.org/0009-0004-7729-7284)
- Baoguo Wu (ORCID: https://orcid.org/0000-0002-4145-4450)
- Yuling Chen (ORCID: https://orcid.org/0000-0002-8399-2070)
- Jiting Yin
- Jian Zhao (ORCID: https://orcid.org/0000-0003-3949-352X)
Institutions
- Peking University (CN)
- Beijing Forestry University (CN)
- Dali University (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-10-15
- DOI
- https://doi.org/10.3389/fpls.2025.1610571
- Citations
- 1
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.80
Funders
- National Natural Science Foundation of China