## 4 Conclusions  
The novelty of this paper is the evaluation of the EfficientNet-B3 model's robustness in AI-based bark classification using CBDS_Small, focusing on environmental impacts-time of day, bark moisture, and cardinal direction on northern red oak, hackberry, and bitternut hickory classification. The analysis of a CBDS_Small dataset revealed that hackberry outperformed northern red oak (90.00% vs. 86.46% accuracy), reversing prior trends from the CentralBark dataset [9], while bitternut hickory consistently exhibited low accuracy (26.76%), aligning with its prior performance (63.38%). These results were influenced by the smaller sample size and repeated imaging under varied conditions. Furthermore, the observed reversal in trends for hackberry vs. northern red oak is likely to stem from its distinct physical characteristics, which facilitate identification across all life stages. Hackberry's characteristic wart-like protrusions are not as prominent on young trees, unlike red oak, where diagnostic features like \"ski tracks\" show pretty early in the tree's life cycle. At the 10-inch DBH threshold used in [PERSON] et al., 2024 northern red oak is more readily distinguishable than hackberry [9]. This difference becomes more pronounced in our study, which targets a 16-18-inch DBH range, where hackberry's prominent features enhance its recognizability in our smaller dataset. and underscore the interplay of species morphology and environmental factors in model performance.  
Bark moisture condition emerged as a significant factor, with dry conditions yielding 8.19% higher accuracy than wet conditions (89.32% vs. 81.13%), as wet bark darkens the normal appearance like northern red oak's \"ski tracks\", hackberry's wart-like texture, and bitternut hickory's imperceptible bark characteristics. Time of day also significantly affected accuracy, with Chi-Square tests confirming strong associations for hackberry (\(\chi^{2}\) = 10.99, \(p\) = \(<\)0.05) and northern red oak (\(\chi^{2}\) = 7.66, \(p\) = 0.02), driven by evening (95.56% for hackberry) and morning (91.67% for northern red oak) light enhancing feature visibility, while afternoon's harsh light reduced northern red oak's accuracy (80.80%). Bitternut hickory's non-significant result (\(\chi^{2}\) = 4.83, \(p\) = 0.09) reflects its morphological challenges, as its subtle features remain difficult to classify across all lighting conditions. Cardinal direction had minimal impact (4.72% maximum difference), suggesting that lighting intensity and angle are more critical than directional shadows.  
These findings highlight the need for robust deep learning models to address environmental variability in AI-based tree identification. Targeted augmentation of the CentralBark dataset with wet bark and afternoon images, particularly for northern red oak, and diverse conditions for bitternut hickory, is essential to improve model generalization. Preprocessing techniques, such as illumination normalization and attention mechanisms, could enhance feature detection under varying light and moisture conditions [2]. Species-specific strategies, including oversampling or transfer learning for bitternut hickory, are crucial to overcome morphological challenges. These improvements are vital for practical applications like UAV-based monitoring [16], enabling reliable automated species identification in dynamic forest environments.