AUTHOR=Deng Longfeng , Wang Jianming , Yin Jiting , Chen Yuling , Wu Baoguo 

TITLE=Construction of crown profile prediction model of Pinus yunnanensis based on CNN-LSTM-attention method

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1567131

DOI=10.3389/fpls.2025.1567131

ISSN=1664-462X

ABSTRACT=Pinus yunnanensis is a significant tree species in southwest China, crucial for the ecological environment and forest resources. Accurate modeling of its crown profile is essential for forest management and ecological analysis. However, existing modeling approaches face limitations in capturing the crown’s spatial heterogeneity and vertical structure. This study aims to propose a novel approach that combines deep learning with a crown competition index to overcome the limitations of traditional models in capturing crown asymmetry and vertical structure, thereby enhancing prediction accuracy. Thus, we developed a hybrid CNN-LSTM-Attention deep learning model combined with a novel Crown Profile Competition Index (CPCI), based on data collected from 629 trees across five age-stratified permanent plots on Cangshan Mountain, Dali, Yunnan Province. Experimental results showed that the hybrid CNN-LSTM and CNN-LSTM-Attention models significantly outperformed the Vanilla LSTM model. In particular, the CNN-LSTM-Attention model achieved the best performance (MSE=0.00755 m2, RMSE=0.08691 m, MAE=0.05198 m, R²=0.98161), with absolute R² improvements of 0.16 and 0.17 over the Vanilla LSTM model by the CNN-LSTM and CNN-LSTM-Attention models, respectively. Additionally, the CNN-LSTM-Attention model demonstrated superior stability and performance in handling directional crown profile datasets. Incorporating CPCI improved prediction accuracy across all models, especially benefiting the Vanilla LSTM model. In conclusion, the proposed hybrid deep learning framework significantly enhances crown profile prediction for Pinus yunnanensis, and the introduction of CPCI provides a more precise representation of vertical and directional crown competition. This improvement facilitates more accurate assessment of tree crown dynamics, which is critical for understanding forest structure and competition.