## 3 Results
### Per-Class Performance  
Tables 3 and 4 summarize the per-class F1 scores for the validation (Validation) and test (Test) sets, respectively. In this section, we primarily focus on the minority classes--namely, ginseng and tree nursery--while also briefly noting changes in major classes. We examine how each method's F1 score changes (\(\Delta\)Val/\(\Delta\)Test) compared to the baseline (NCB + FPE).  
RPE enhances training diversity by introducing random offsets during patch selection. While this approach generally improves performance across various classes, including several major ones, our analysis focuses on its effects on minority classes. For instance, ginseng (1\(\times\)) showed substantial gains (+0.0696/+0.0788). However, tree nursery (2\(\times\)) exhibited a performance drop (\(-0.2025/-0.0795\)), indicating that RPE does not consistently benefit all minority classes.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{**(a) Validation Set**} & \multicolumn{4}{c}{**Scale: 1\(\times\)**} & \multicolumn{4}{c}{**Scale: 2\(\times\)**} \\ \cline{2-7}
**Method** & **Accuracy** & **Macro F1** & **Kappa** & **Accuracy** & **Macro F1** & **Kappa** \\ \hline NCB + FPE (baseline) & 0.9645 & 0.9130 & 0.9598 & 0.8392 & 0.7097 & 0.8162 \\ NCB + RPE & 0.9821 & 0.9632 & 0.9797 & 0.9659 & 0.8694 & 0.9613 \\ CBS + FPE & 0.9817 & 0.9621 & 0.9792 & 0.9502 & 0.9169 & 0.9415 \\ CBS + RPE (Ours) & 0.9910 & 0.9815 & 0.9898 & 0.9794 & 0.9663 & 0.9782 \\ \hline \hline \multicolumn{1}{c}{**(b) Test Set**} & \multicolumn{4}{c}{**Scale: 1\(\times\)**} & \multicolumn{4}{c}{**Scale: 2\(\times\)**} \\ \cline{2-7}
**Method** & **Accuracy** & **Macro-F1** & **Kappa** & **Accuracy** & **Macro-F1** & **Kappa** \\ \hline NCB + FPE (baseline) & 0.6957 & 0.6168 & 0.6506 & 0.4857 & 0.4394 & 0.4277 \\ NCB + RPE & 0.7531 & 0.6775 & 0.7130 & 0.6638 & 0.5672 & 0.5594 \\ CBS + FPE & 0.7291 & 0.6798 & 0.6891 & 0.7814 & 0.6707 & 0.7434 \\ CBS + RPE (Ours) & 0.7811 & 0.7043 & 0.7451 & 0.8033 & 0.7166 & 0.7687 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall ablation performance metrics: accuracy, macro F1 score, and Kappa.  
CBS addresses class imbalance by ensuring equal sampling probability across all classes, thereby providing more consistent learning opportunities for minority classes. While the number of training examples for most major classes is moderately reduced by undersampling, their performance sometimes improves--likely because balancing helps the model learn clearer class boundaries. Ginseng (2\(\times\)) shows a large improvement of \((+0.4000/+0.4332)\). In contrast, ginseng (1\(\times\)) sees an improvement of \((+0.0600/+0.0123)\), which is on par with other methods on the validation set but significantly lower on the test set. Tree nursery (2\(\times\)) also improves to \(+0.4086\) on the validation set, but the gain is limited to \(+0.0349\) on the test set. These results suggest that the use of CBS alone may lead to performance gaps between validation and test sets in certain minority classes.  
When RPE and CBS are combined, the proposed method achieves top or near-top F1 scores for minority classes under both 1\(\times\)/2\(\times\) conditions on the validation and test sets, and it also frequently attains the highest scores among the major classes. Overall, this indicates stable and consistent improvement for minority classes. In particular, tree nursery(2\(\times\)) rises to \((+0.6171/+0.4044)\), yielding the highest observed performance gain. Further interpretation of these results is presented in Section 4.1.