## 3 Results and Discussion  
Analysis of the CBDS_Small dataset revealed classification accuracies that both aligned with and diverged from trends observed in our prior research using the larger CentralBark dataset [9]. As presented in Table 1, hackberry and northern red oak exhibited a reversal in relative performance compared to earlier studies. Specifically, hackberry achieved a classification accuracy of 90.00%, outperforming northern red oak at 86.46% by a margin of 3.54%. This shift is notable, given that northern red oak previously demonstrated higher accuracy (91.28%) in the CentralBark dataset. However, the smaller sample size of the current dataset--comprising 96 images for northern red oak, 84 for hackberry, and 72 for bitternut hickory--warrants cautious interpretation. The dataset involved repeated imaging of the same trees under varying light conditions, moisture levels, and cardinal directions, potentially amplifying specific environmental influences not fully captured in the larger dataset. Consistent with expectations, bitternut hickory exhibited the lowest classification accuracy at 26.76%, aligning with its prior performance (63.38%) and underscoring persistent challenges in accurately classifying this species. The low accuracy for bitternut hickory may stem from its subtle bark features, which are less visually distinct than those of hackberry or northern red oak, particularly under variable conditions. These findings suggest that while hackberry's distinctive features may confer an advantage in smaller, controlled datasets, scaling up data collection and diversifying environmental conditions are critical to stabilizing model performance across species. Further analysis examined the influence of three environmental variables--cardinal direction of image capture, bark moisture condition, and time of day--on classification accuracy. Table 2 shows that no consistent pattern emerged to suggest that cardinal direction significantly impacted model performance. The maximum observed difference in accuracy was 4.72%, with east-facing images yielding the highest accuracy, 88.42%, and west-facing images the lowest, 83.70%. north- and south-facing images had accuracies of 84.61% and 85.95%, respectively. These results align with our hypothesis that directional effects, particularly those tied to sunrise (east) and sunset (west), may introduce variability due to differences in lighting and shadow patterns. However, the modest variation suggests that cardinal direction is a less dominant factor than anticipated, possibly because the EfficientNet-B3 model [10] is robust to subtle lighting differences caused by directional orientation. This finding has practical implications for field data collection, suggesting that strict control of cardinal direction may not be necessary, thereby allowing for greater flexibility in image capture protocols. Nonetheless, the slight advantage of east-facing images may warrant further investigation into whether morning light enhances bark feature visibility, particularly for species with complex textures. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Species** & \begin{tabular}{c} **CBDS\_Small** \\ **Accuracy (\%)** \\ \end{tabular} & \begin{tabular}{c} **\#** \\ **\end{tabular} & \
\begin{tabular}{c} **CentralBark** \\ **Accuracy (\%)** \\ \end{tabular} &
\begin{tabular}{c} **\#** \\ \end{tabular} \\ \hline Northern Red Oak & 86.46 (3.30) & 96 & 91.28 & 1158 \\ Hackberry & 90.00 (2.88) & 84 & 75.53 & 846 \\ Bitternut Hickory & 26.76 (4.73) & 72 & 63.38 & 609 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Raw classification accuracy for the CBDS_Small vs. CentralBark using the EfficientNET-b3 model. Analysis of bark moisture conditions revealed a pronounced impact on classification accuracy, as shown in Table 3. Images captured under dry conditions yielded an accuracy of 89.32%, surpassing wet conditions by 8.19% (81.13% accuracy).