## 4 Experiments
### Model Performance

Table 1: Comparisons of model’s performance on the _validation_ and the _MBS_ dataset. The best performance of MIoU is **boldfaced**. Standard deviations are given in parentheses. Figure 6: Train (solid) and validation (dashed) learning curves of our experiments. The \(x\)-axis and \(y\)-axis mean epoch and validation MIoU, respectively. Each column corresponds to DeepLab V3+, U-Net, and SegNet. Each row corresponds to RGB images, RGB with RE, RGB with NIR, and RGB with RE and NIR, respectively. Note that the \(x\)-axis limits of the first and other columns are set differently. Figure 7 provides visualizations of model predictions, along with their ground truth labels for three representative examples from the MBS dataset. It is observable that the DeepLab V3+ predictions using RGB inputs most closely match the ground truth. Figure 7: Visualization of model predictions for three representative examples from the MBS dataset. There were input images and ground truth in the first and second columns. Prediction results are represented from the third column to the sixth column. Results of DeepLab V3+, U-Net, and SegNet are repeatably visualized from the first row to the final row. The area occupied by each input in Maebonsan is about 1.77 ha. Each input image width and height of DeepLab V3+, U-Net, and SegNet is 513, 572, and 224, respectively. To verify whether DeepLab V3+ generalizes across different times and regions, we evaluated its predictive performance on the GNM dataset collected from Gwinemi. As shown in Table 2, DeepLab V3+ with RGB and RE performed the best. We found that the RE wavelength has a positive effect on the detection performance of images collected from different spatiotemporal conditions. In addition, the standard deviation of DeepLab V3+ with RGB and RE was the smallest. Further, in Figure 8, we provided visualizations of predictions on the GNM dataset. The example in the first row included greenhouses. We found that DeepLab V3+ with RGB and RE wavelengths succeeds in distinguishing the greenhouses from cabbage fields. In addition, the second example of Figure 8 demonstrates that the DeepLab V3+ is capable of distinguishing cabbage fields from weeds (dark green). Last but not least, we could see from the third row's example that the land left fallow in the lower left part of the image is correctly classified as not growing cabbage. The ABD dataset was composed of images photographed in Anbanduck. As can be seen from the data in Table 2, DeepLab V3+ with RGB and RE performed the best like the GNM dataset. Also, we observed that adding RE to the input improves the detection performance. Figure 9 shows that our model successfully distinguishes fields of weeds, forest regions, and wind turbines from cabbage fields. \begin{table}
\begin{tabular}{c c c c} \hline
**Input Wavelengths** & **Model** & **GNM Dataset MIoU** & **ABD Dataset MIoU** \\ \hline \multirow{2}{*}{DeepLab V3+} & RGB & 0.9072 & 0.7294 \\  & & (0.0045) & (0.0764) \\ \multirow{2}{*}{U-Net} & RGB & 0.8999 & 0.4734 \\  & RGB & (0.0076) & (0.0689) \\ \multirow{2}{*}{SegNet} & RGB & 0.8191 & 0.4873 \\  & RGB & (0.0210) & (0.2066) \\ \hline \multirow{2}{*}{textbffDeepLab V3+} & **RGB + RE** & **0.9097** & **0.8223** \\  & & **(0.0030)** & **(0.0483)** \\ \multirow{2}{*}{U-Net} & RGB +RE & 0.8983 & 0.7459 \\  & RGB +RE & (0.0030) & (0.0605) \\ \multirow{2}{*}{SegNet} & RGB + RE & 0.7921 & 0.6435 \\  & & (0.0288) & (0.0611) \\ \hline \multirow{2}{*}{DeepLab V3+} & RGB + NIR & 0.8605 & 0.7812 \\  & & (0.0221) & (0.0340) \\ \multirow{2}{*}{U-Net} & RGB +NIR & 0.8912 & 0.5933 \\  & & (0.0174) & (0.0976) \\ \multirow{2}{*}{SegNet} & RGB + NIR & 0.7054 & 0.1440 \\  & & (0.0214) & (0.0873) \\ \hline \multirow{2}{*}{DeepLab V3+} & RGB + RE + NIR & 0.8525 & 0.3084 \\  & & (0.0246) & (0.1622) \\ \multirow{2}{*}{U-Net} & RGB + RE + NIR & 0.8665 & 0.3121 \\  & & (0.0326) & (0.1901) \\ \multirow{2}{*}{SegNet} & RGB + RE + NIR & 0.7048 & 0.5444 \\  & & (0.0443) & (0.2246) \\ \hline \end{tabular}
\end{table}