## 4 Discussion
### Difficulty-Based Performance Analysis on Minority Classes
#### 4.1.1 Class Difficulty Assessment  
Figure 10 shows representative parcel images of ginseng and tree nursery, making it straightforward to observe the textural differences between these classes. Ginseng parcels typically maintain uniform coloration and vegetation patterns, whereas tree nursery parcels feature uneven textures; the color and shape can vary considerably according to each seedling's growth stage and cultivar, and multiple distinct traits are often intermingled at the parcel level. To quantitatively support this visual assessment, Table 7 summarizes the validation (same domain) metrics obtained under a baseline method in the 1\(\times\) and 2\(\times\) environments. In particular, we compare the difficulty of classifying ginseng and tree nursery using the F1 score, partial confusion matrices, and average confidence, excluding the test set because the domain discrepancy made it unsuitable for difficulty analysis. Ginseng (1\(\times\) ) recorded an F1 score of 0.9043, a correct-prediction rate of about 89.7%, and an average confidence of 0.8837, signifying high predictive performance overall. Its par \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Peak CPU Memory (MB)** & **Peak GPU Memory (MB)** & **Time per Epoch (s)** \\ \hline NCB + RPE (baseline) & 1238.5 & 3010.8 & 1564.3 \\ NCB + RPE & 1237.1 & 3010.1 & 1584.8 \\ CBS + FFE & 1239.3 & 3005.4 & 1565.7 \\ CBS + RPE (Ours) & 1238.4 & 3005.2 & 1582.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Memory usage and training time for different configurations. cel interior remains relatively uniform, which likely facilitated easy training and inference, making Ginseng (1\(\times\)) an easy class to classify. Ginseng (2\(\times\)) is still the same ginseng class, but due to 2\(\times\) upscaling, the patch coverage area was effectively reduced to less than half, leading to an F1 score of 0.5500, a correct prediction rate of 37.9%, and an average confidence of 0.7680. These results indicate a noticeable drop compared to ginseng (1\(\times\)), with most misclassifications going to majority classes such as mulched field. Consequently, ginseng (2\(\times\)) can be deemed a normal difficulty level. Meanwhile, tree nursery (1\(\times\)) obtains an F1 score of 0.4694, a correct prediction rate of 39.1%, and an average confidence of 0.6562, reflecting generally low predictive performance. Most misclassifications occurred as orchard. Within a single parcel, the color and shape are highly variable, depending on each seedling's growth stage and cultivar, forming an \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pred** & **GT** & **Ginseng (1\(\times\))** & **Ginseng (2\(\times\))** & **Tree Nursery** & **Tree Nursery** \\  & & & & **(1\(\times\))** & **(2\(\times\))** \\ \hline  & \multicolumn{1}{c}{Plowed Rice Paddy} & 0 & 0 & 0 \\  & \multicolumn{1}{c}{Transplanted Rice Paddy} & 0 & 0 & 0 \\  & \multicolumn{1}{c}{Growing Rice Paddy} & 0 & 0 & 0 \\  & \multicolumn{1}{c}{Harvested Rice Paddy} & 0 & 0 & 0 \\  & \multicolumn{1}{c}{Plowed Field} & 4 & 0 & 0 \\  & \multicolumn{1}{c}{Mulched Field} & 2 & 30 & 0 \\  & \multicolumn{1}{c}{Growing Field} & 0 & 4 & 0 & 1 \\  & \multicolumn{1}{c}{Tree Nursery} & 0 & 0 & 27 & 8 \\  & \multicolumn{1}{c}{Orchard} & 0 & 2 & 41 & 59 \\  & \multicolumn{1}{c}{Greenhouse} & 0 & 0 & 0 & 0 \\  & \multicolumn{1}{c}{Ginseng} & 52 & 22 & 1 & 0 \\ \hline  & \multicolumn{1}{c}{Validation F1 score} & 0.9043 & 0.5500 & 0.4694 & 0.2025 \\ \hline  & \multicolumn{1}{c}{Average Correct Confidence} & 0.8837 & 0.7680 & 0.6562 & 0.6170 \\ \hline  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \end{tabular}
\end{table} 
Table 7: Partial confusion matrix for minority classes under 1\(\times\) and 2\(\times\) conditions, with F1 scores and average correct confidence shown below. Figure 10: Parcel images from two visually distinct minority farmland classes. uneven texture that makes it difficult for the model to recognize tree nursery as a single class.