## 6 Evaluation Experiment
### Baseline
#### 6.2.2 Rice Blast  
Table 5 represents the confusion matrix for the Rice blast test subset images. Numbers in the parentheses correspond to recognition rates on the diagonal cells and the false recognition rates on the other cells. Although high accuracy was obtained in RL, the accuracy was low in RB. The false recognition between RB and RL is greater than in other combinations of classes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**ID** & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Training & 0.66 & 0.20 & 0.93 & 0.66 & 0.80 & 0.29 & 0.29 & 0.13 & 0.01 & 0.25 & 0.82 \\ Test & 0.69 & 0.03 & 0.88 & 0.48 & 0.70 & 0.04 & 0.04 & 0.09 & 0.00 & 0.11 & 0.74 \\ \hline
**ID** & **11** & **12** & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** & **Mean** \\ \hline Training & 0.51 & 0.87 & 0.50 & 0.17 & 0.84 & 0.34 & 0.48 & 0.71 & 0.27 & 0.40 & 0.48 \\ Test & 0.01 & 0.64 & 0.16 & 0.02 & 0.66 & 0.03 & 0.06 & 0.01 & 0.01 & 0.16 & 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: IoU scores for the respective Cityscapes classes.  
Figure 15: Confusion matrix for Cityscapes test subset images.  
Table 6 presents the semantic segmentation results evaluated using IoU scores for the respective Rice blast classes. The respective mIoU scores for the training and test subset images are 0.87 and 0.45. The respective highest IoU scores for the training and test RL images are 0.94 and 0.72. By contrast, the lowest IoU scores for the training and test RB images are, respectively, 0.77 and 0.10. The IoU scores of RB for the training and test subset images are, respectively, 0.10 and 0.35 lower than the mean score. We consider that the low IoU score for RB is attributable to its lower frequency of occurrence than other classes.  
Figure 16: Representative successful Cityscapes images: source images (**left**), GT (**center**), and results (**right**).  
Figure 17: Representative failed Cityscapes images: source images (**left**), GT (**center**), and results (**right**).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **BG** & **RL** & **RB** \\ \hline BG & 43,544,288 & 32,753,681 & 14,875 \\  & (57.06\%) & (42.92\%) & (0.02\%) \\ \hline RL & 2,926,683 & 89,268,832 & 38,594 \\  & (3.17\%) & (96.79\%) & (0.04\%) \\ \hline RB & 26,151 & 127,392 & 28,898 \\  & (14.33\%) & (69.83\%) & (15.84\%) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix for Rice blast test subset images [pixels].  
Figures 18 and 19 respectively present representative successful and failed test subset images. The mIoU threshold to classify these cases was set as 0.10. As the depicted images in Figure 18, RB pixels were roughly segmented from the RL regions. This result indicates the relation that RB pixels are apparent in RL regions. By contrast, as depicted images in Figure 19, false segmentation pixels exist in small RB regions with low segmentation accuracy. False segmentation pixels are apparent in dead leaf tips and RB pixels that are segmented as BG pixels. This finding from experimentation demonstrates the difficulty of learning object boundaries between RB and RL pixels.