## 4 Discussion
### Difficulty-Based Performance Analysis on Minority Classes
#### 4.1.2 Effects of Each Method According to Class Difficulty  
Based on the class difficulties defined in Section 4.1.1, we compare the effects of RPE and CBS in Table 8. All performance values are presented in the form of (\(\Delta\)Val/\(\Delta\)Test). Ginseng (1\(\times\)) is classified as an easy class. When using CBS alone, the validation F1 score improved by (+0.0600), but the test F1 score increased by only (+0.0123), indicating a relatively modest improvement on the test set. This supports the observation in [56] that excessive oversampling for a low-difficulty class can induce model bias and degrade generalization. In contrast, applying RPE alone--via random patch extraction at various positions--enhanced data diversity and yielded balanced performance gains of (+0.0696/+0.0790), improving the model's generalization ability. When RPE and CBS were combined, the data diversity from RPE offset the overfitting tendency sometimes seen with CBS alone, yielding the highest domain adaptation gains (+0.0581/+0.1241). Under 2\(\times\) upscaling, ginseng (2\(\times\)) suffers reduced context information and is considered a normal-difficulty class. Even with CBS alone, it achieved (+0.4000/+0.4332), which falls only slightly short (\(-0.0500/-0.0042\)) of the RPE + CBS combination. Although RPE alone produced stable gains (+0.3015/+0.3051), it was more limited compared to CBS. Combining RPE and CBS led to the highest performance (+0.4500/+0.4374). These results imply that the class is neither extremely low in difficulty nor lacking distinct structural features, so oversampling can be applied without causing overfitting--unlike the oversampling tendency observed in ginseng (1\(\times\)). Hence, for oversampling to work effectively, a sufficiently moderate difficulty level and clear class texture are both required. Tree nursery (1\(\times\)) is deemed a difficult class. CBS alone improved performance by (+0.3185/+0.2750), whereas RPE alone provided a somewhat limited gain (+0.3119/ +0.1579). This indicates that as class difficulty increases, merely increasing data diversity (RPE) without additional training cycles is insufficient, and repeated sampling for minority classes (CBS) becomes more vital. Combining RPE and CBS offers both the stability of repeated sampling and enhanced data diversity, producing the greatest overall improvement (+0.4409/+0.3239). \begin{table}
\begin{tabular}{l l l l l l} \hline
**Class** & **Difficulty** & **Baseline (Val/Test)** & **RPE (\(\Delta\)Val/\(\Delta\)Test)** & **CBS (\(\Delta\)Val/\(\Delta\)Test)** & **RPE + CBS (\(\Delta\)Val/\(\Delta\)Test)** \\ \hline Ginseng (1\(\times\)) & Easy & 0.9043/0.7859 & +0.0696/+0.0790 & +0.0600/+0.0123 & +0.0581/+0.1241 \\ Ginseng (2\(\times\)) & Normal & 0.5500/0.5135 & +0.3015/+0.3051 & +0.4000/+0.4332 & +0.4500/+0.4374 \\ Tree Nursery (1\(\times\)) & Difficult & 0.4694/0.0980 & +0.3119/+0.1579 & +0.3185/+0.2750 & +0.4409/+0.3239 \\ Tree Nursery (2\(\times\)) & Very Difficult & 0.2025/0.0795 & \(-0.2025/-0.0795\) & +0.4086/+0.0349 & +0.6171/+0.4044 \\ \hline \end{tabular}
\end{table} 
Table 8: Baseline F1 score accuracy and performance improvements (\(\Delta\)Val/\(\Delta\)Test) for RPE, CBS, and RPE + CBS on minority classes, categorized by class difficulty. Tree nursery (2\(\times\)) is considered very difficult. It remains the same tree nursery class but, under the 2\(\times\) condition, suffers a significant loss of context information. Applying RPE alone further intensified the model's confusion, failing to make any correct predictions for this class, while CBS alone produced a substantial improvement on the validation set (+0.4086) but had limited effects on the test set (+0.0349), consistent with [57], which notes that oversampling of highly complex minority classes may be restricted or counterproductive. These findings show that neither method alone can overcome both context loss and class uncertainty.