Overcoming Structural and Environmental Challenges in Identifying Brazilian Savanna Tree Species Using Deep Learning

The Brazilian Cerrado, a global biodiversity hotspot, faces persistent challenges in species identification due to the limitations of phenology‑dependent methods in its seasonally dynamic landscapes. This study evaluates the performance of convolutional neural networks (CNNs) for bark‑image‑based classification of three ecologically prominent tree species: Apuleia leiocarpa , Astronium fraxinifolium , and Vochysia haenkeana . We compiled 1,515 bark images from individual trees (DBH ≥ 25 cm) during the 2023–2024 rainy seasons and applied data augmentation and normalization. Using the MobileNetV2 architecture, we trained and validated the model with metrics including Accuracy, Precision, Recall, F1‑score, Confusion Matrix, ROC/AUC curves, and t‑SNE projections. The model achieved an overall accuracy of 90.52%. Bark morphological complexity strongly influenced classification: V. haenkeana , with distinct patterns, showed the highest performance (Precision 1.00, Recall 0.94), while A. fraxinifolium and A. leiocarpa , which share more convergent bark traits, exhibited higher misclassification rates (22.54%). These results demonstrate how interspecific bark variability affects CNN discrimination and confirm that intrinsic bark heterogeneity (e.g., rhytidome texture, rugosity, color patterns, scars) and environmental variation increase classification difficulty. Our findings highlight the potential of bark‑based deep learning models as phenology‑independent tools for large‑scale forest inventories and biodiversity monitoring in complex ecosystems. A key limitation is the dataset’s restriction to a single seasonal period, underscoring the need for broader temporal sampling. This study reinforces the role of deep learning in delivering scalable and accurate solutions for ecological research and conservation in understudied biodiversity hotspots.