Exploring the potential of transfer learning for leaf inclination angle distribution assessment from leveled digital photography

Leaf inclination angle distribution (LIAD) is a key descriptor of canopy structure that governs light interception, evapotranspiration, rainfall capture, and biomass production. Accurate LIAD estimates support ecophysiological modeling, enhance crop phenotyping, and precision agriculture; yet LIAD remains among the least constrained parameters in land-surface models due to measurement challenges. We explored a transfer-learning framework for classifying LIAD into five classic types from leveled RGB images. The full model configuration uses an ImageNet-pretrained EfficientNet-B0 backbone with custom fully connected layers to capture leaf-orientation features. The last five backbone stages were fine-tuned while earlier stages remained frozen to preserve generic visual representations. To assess each component's contribution, we conducted a component-wise ablation over four elements: backbone fine-tuning, custom classifier head, image-level augmentation, and mixed-sample training. On the model-development held-out test set, the configurations performed comparably, with macro-F1 values ranging from 85.26% to 88.34%, indicating that no single component dominated performance. Mixed-sample training was the most beneficial component, as its removal caused the largest macro-F1 reduction of 2.02 percentage points. In contrast, disabling image-level augmentation slightly improved held-out test performance and produced the highest component-ablation result, with 88.16% accuracy and 88.34% macro-F1. On the independent validation dataset, the same reference configuration achieved 52.83% accuracy, outperforming two prior machine-learning approaches by 27.43 and 21.08 percentage points. These results indicate that transfer learning can support LIAD classification from leveled RGB images, while further validation across species, sites, and acquisition conditions is needed before broader operational use in canopy-structure assessment and ecological or precision-agriculture workflows.