ArticleFrontiers in plant science2026
Proximal policy optimization integrated with Bayesian optimization for stand structure optimization in
Article in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Secondary forests are often characterized by irrational stand structures and spatial imbalances, which constrain the full realization of their ecological functions. Existing deep learning approaches for stand structure optimization still face several limitations, including low computational efficiency, Q-value overestimation, and insufficient consideration of site-specific factors in felling constraints. Methods: To address these issues, this study focused on secondary Results: The results demonstrated that the PPO-BO algorithm consistently achieved 19 higher maximum objective function values across five circular plots (0.4475, 0.5075, 0.5146, 0.5133, and 0.5255) compared with the DQN algorithm (0.4421, 0.4973, 0.5051, 0.4947, and 0.5057), while also reducing overall training costs. Following selective felling, the spatial structure indices of each plot improved markedly, with the overall stand structure gradually approaching that of natural forests. Discussion: Overall, this study provides new insights into the optimization of stand structure in secondary forests and enriches the theoretical and methodological framework for sustainable forest management.
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