CLIP-TLNet: Canopy light interception prediction with Transformer-LSTM network through 3D complexity-temporal dynamics modeling.

Accurate prediction of canopy light interception is essential for advancing precision management in planted forests. However, conventional approaches that rely on manual pruning strategies are limited in scalability and fail to effectively capture the dynamic interactions between canopy structure and light distribution. We propose a novel Canopy Light Interception Prediction with Transformer-LSTM Network(CLIP-TLNet) that combines 3D spatial complexity quantification with temporal modeling to predict light distribution within individual tree canopies over time. Leveraging multi-sensor UAV-derived LiDAR point clouds and synchronized photometric measurements from triploid poplar plantations, we develop a regional canopy complexity algorithm based on multi-scale fractal dimension analysis, enabling precise quantification of structural heterogeneity. This complexity metric enhances model interpretability and improves learning performance by characterizing how canopy architecture influences light penetration. In parallel, the spatio-temporally decoupled Transformer-LSTM architecture effectively captures temporal trends while maintaining spatial awareness, yielding a Mean Absolute Percentage Error (MAPE) of 6.8 % and outperforming the second-best CNN-LSTM model by a 33.6 % reduction in Root Mean Square Error (RMSE).Ablation studies confirm that incorporating canopy complexity as a structural prior caused a 20.6 % increase in MAPE upon its removal, underscoring its critical role in predictive performance. By enabling data-driven, complexity-aware pruning strategies and temporally optimized interventions, this framework bridges static structural assessment with dynamic environmental response modeling. It offers a powerful tool for precision silviculture and intelligent canopy management in plantation forestry.