## 3 Results  
Japanese Oak Segmentation Using the ResU-Net Model and the UAV Datasets Acquired before the Leaf Color Change (without and with Augmentation)  
When the ResU-Net model was trained on the September UAV datasets taken before the change in leaf color, in terms of OA, recall, the F1-score, and IoU, the performance of the model using the non-augmented dataset was significantly better, with values of 0.90, 0.88, 0.81, and 0.68, respectively. The corresponding values of the model using the augmented UAV dataset were 0.87, 0.66, 0.71, and 0.55; only the precision was slightly higher. The accuracy scores obtained in the identification of Japanese oak using the ResU-Net model and September UAV datasets (without and with augmentation) are shown in Table 1 and visualized in Figure 6.  
According to the results of the confusion matrices shown in Figure 7, the ResU-Net model trained with the non-augmented September UAV dataset achieved an accurate prediction of Japanese oak of 87.70% of the cases but misclassified 11.03% and 1.27% of the Japanese oak as background and border, respectively. Conversely, 7.81% and 31.14% of the background and border identifications were misidentified as Japanese oak. Meanwhile, when the augmented September UAV dataset was used to train the ResU-Net model, accurate predictions were obtained in only 66.20% of the cases, with Japanese oak classified incorrectly as background and border at rates of 31.91% and 1.89%, which were higher compared to when using the non-augmented UAV dataset. However, the rates of misidentifications of background and border as Japanese oak (5.68% and 20.10%, respectively) were lower than those of the non-augmented September UAV dataset. Particularly, the non-augmented UAV dataset misclassified other deciduous broadleaf species such as Japanese linden (_T. japonica_) and castor analia (_K. septembolus_) as Japanese oak at a higher rate than the augmented UAV dataset. A sample patch of misdetection on the predicted images by the ResU-Net model using both non-augmented and augmented September UAV datasets is visualized in Figure 8. The visualization of separation of Japanese oak and the border class using the watershed algorithm is shown in Figure 9. \begin{table}
\begin{tabular}{c c c c c} \hline
**UAV** & **OA** & **Precision** & **Recall** & **F1-Score** & **IoU** \\ \hline September (non-augmented) & 0.90 & 0.75 & 0.88 & 0.81 & 0.68 \\ September (augmented) & 0.87 & 0.76 & 0.66 & 0.71 & 0.55 \\ \hline \end{tabular}
\end{table} 
Table 1: OA, precision, recall, F1-score and IoU values of identifying Japanese oak crowns in an uneven-aged mixed forest by using non-augmented and augmented September UAV datasets and ResU-Net algorithm (calculated based on the validation dataset). Figure 6: Summarized accuracy scores (OA, precision, recall, F1-score and IoU values) of identifying Japanese oak crowns in an uneven-aged mixed forest by using non-augmented and augmented September UAV datasets and ResU-Net algorithm (calculated based on the validation dataset). The highest values are in bold. Figure 8: Visualization of the predicted results of Japanese oak crown using ResU-Net model and September UAV datasets. (**a**) Testing RGB image; (**b**) labeled mask; (**c**) prediction using test image (non-augmented UAV dataset); (**d**) misclassified regions (non-augmented UAV dataset); (**e**) prediction using test image (augmented UAV dataset); and (**f**) misclassified regions (augmented UAV dataset). Figure 7: Confusion matrix of Japanese oak segmentation through ResU-Net model using (**a**) non-augmented September UAV dataset and (**b**) augmented September UAV dataset.