## 3 Results and Discussion
This disparity was anticipated for the selected species--northern red oak, hackberry, and bitternut hickory--which exhibit slate-gray bark when dry but darken significantly when wet, altering their visual appearance. For northern red oak, the characteristic \"ski track\" furrows [14] transitioning from slate gray to off-white become less distinct under wet conditions, potentially confusing the model. Similarly, hackberry's wart-like protuberances [14] a defining feature, may appear subdued when wet, reducing contrast. Bitternut hickory, with its tightly packed bark and narrow slits, is particularly susceptible to misclassification when wet, as moisture masks its subtle peeling texture [15]. The classification accuracies for northern red oak, hackberry, and bitternut hickory across morning (07:00-10:00), afternoon (12:00-15:00), and evening (17:00-20:00), as shown in Table 4, reveal the impact of lighting conditions on the EfficientNet-B3 model's performance in bark-based tree identification. Northern red oak achieved the highest accuracies in morning (91.67%) and evening (90.00%) but dropped to 80.80% in the afternoon, likely due to harsh midday light creating high-contrast shadows that obscure its deep furrows. Hackberry exhibited a progressive increase, from 85.00% in the morning to 95.56% in the evening, where low-angle light enhanced its bumpy protuberances, supported by a larger evening sample (45 images vs. 30 for morning/afternoon). Bitternut hickory consistently performed poorly (20.83-33.33%), with its faint bark slits and peeling challenging the model across all conditions, exacerbated by a smaller evening sample (25 images). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time** & **Species** & **Accuracy (\%)** & \# \\ \hline Morning & Northern red oak & 91.67 (5.02) & 30 \\  & Hackberry & 85.00 (6.48) & 30 \\  & Bitternut hickory & 20.83 (7.37) & 30 \\ Afternoon & Northern red oak & 80.80 (6.63) & 30 \\  & Hackberry & 86.67 (6.17) & 30 \\  & Bitternut hickory & 33.33 (8.56) & 30 \\ Evening & Northern red oak & 90.00 (5.45) & 30 \\  & Hackberry & 95.56 (3.04) & 45 \\  & Bitternut hickory & 26.00 (8.75) & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy by time of day. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Direction** & **Accuracy (\%)** \\ \hline North & 84.61 (2.38) \\ West & 83.70 (2.49) \\ South & 85.95 (2.47) \\ East & 88.42 (2.15) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model performance by cardinal direction across all species. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Moisture Condition** & **Accuracy (\%)** & \# \\ \hline Dry & 74.86 (3.54) & 140 \\ Wet & 66.67 (4.05) & 108 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Impact of bark hydration on prediction accuracy across all species. These results align with prior findings, where bark moisture significantly reduced accuracy by 8.19% in wet conditions (Table 3), likely interacting with lighting effects (e.g., wet bark in afternoon light worsening northern red oak's performance). Cardinal direction had minimal impact (4.72% maximum difference, Table 2), emphasizing time of day as a more critical variable. The species' morphological differences--northern red oak's prominent furrows, hackberry's distinctive protuberances, and bitternut hickory's fine features--explain their varying sensitivities to lighting, with hackberry's robustness contrasting with bitternut hickory's persistent challenges. The findings underscore the need for robust models to handle lighting variations in AI-based tree identification. Augmenting the CentralBark dataset [9] with diverse lighting conditions, particularly afternoon images for northern red oak and varied conditions for bitternut hickory, is essential [3]. Techniques such as illumination normalization or attention mechanisms can enhance feature detection, while species-specific strategies (e.g., oversampling for bitternut hickory) address morphological challenges.