## 4 Experiments
### Model Performance  
We used the mean intersection over union (MIoU) metric [17] to quantify the performance of the semantic segmentation models used in this study. The formula of MIoU for a dataset of \(N\) images is defined as follows:  
\[MIoU=\frac{1}{N}\sum_{i=1}^{N}\frac{n_{11}^{(i)}}{n_{1,}^{(i)}+n_{1,}^{(i)}-n_ {11}^{(i)}} \tag{2}\]where \(n_{11}^{(i)}\) is the number of correctly classified pixels in \(i\)th image, \(n_{1. }^{(i)}\) is the number of ground truth pixels labeled as cabbages, and \(n_{1. }^{(i)}\) is the number of pixels predicted as cabbage fields. Best hyperparamters were chosen based on the validation MIoU metric. We checked the MIoU score after every training epoch, and saved the best model checkpoint for further inference on the MBS, GNM, and ABD datasets. We compared the train and validation learning curves of our experiments in Figure 6. It could be observed that the learning curves of DeepLab V3+ shown in Figure 6a,d,g,j exhibited the fastest rate of convergence compared to other models. Also, the fluctuation ranges of the DeepLab V3+ validation MIoU were smaller than other models. In addition, as could be seen in Figure 6f,i,l, SegNet had wide fluctuation ranges and were unstable after epoch 80. As shown from the first row to the fourth row in Figure 6, the more the number of input wavelengths were, the wider fluctuation ranges occurred. Reasons are to be given in Section 5.2. As shown in Figure 6j-l, it can be evaluated that models with NIR wavelength suffer from overfitting. To demonstrate the applicability of our semantic segmentation framework, we should measure how the framework generalizes well for images in three test datasets under different spatiotemporal conditions. Each combination of input data and models was repeated ten times with different random seeds, and we report the average and standard deviations of the MIoU metric. As shown in Table 1, DeepLab V3+ with RGB outperformed both U-Net and SegNet on the MBS dataset. Moreover, we observed better performances in all three models when using RGB images rather than the other input wavelengths. Although the best validation MIoU was the performance of U-Net with RGB and RE, we demonstrated that DeepLab V3+ has more generalizability than U-Net and SegNet based on the MBS dataset MIoU. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Input Wavelengths**} & **The Number of Parameters** & **Validation** & **MBS Dataset** \\  & & **of Parameters** & **MIoU** & **MIoU** \\ \hline \multirow{2}{*}{**DeepLab V3+**} & \multirow{2}{*}{**RGB**} & **54,700,434** & **0.9021** & **0.8997** \\  & & & **(0.0028)** & **(0.0183)** \\ \multirow{2}{*}{U-Net} & \multirow{2}{*}{RGB} & 40,446,786 & 0.8979 & 0.8455 \\  & & & (0.0023) & (0.0187) \\ \multirow{2}{*}{SegNet} & \multirow{2}{*}{RGB} & 29,444,166 & 0.8506 & 0.4851 \\  & & & (0.0198) & (0.1298) \\ \hline \multirow{2}{*}{DeepLab V3+} & \multirow{2}{*}{RGB + RE} & 54,700,722 & 0.8974 & 0.8483 \\  & & & (0.0025) & (0.0466) \\ \multirow{2}{*}{U-Net} & \multirow{2}{*}{RGB + RE} & 40,447,362 & 0.9108 & 0.8042 \\  & & & (0.0028) & (0.0215) \\ \multirow{2}{*}{SegNet} & \multirow{2}{*}{RGB + RE} & 29,444,742 & 0.8676 & 0.6604 \\  & & & (0.0051) & (0.0482) \\ \hline \multirow{2}{*}{DeepLab V3+} & \multirow{2}{*}{RGB + NIR} & 54,700,722 & 0.8768 & 0.8741 \\  & & & (0.0097) & (0.0396) \\ \multirow{2}{*}{U-Net} & \multirow{2}{*}{RGB + NIR} & 40,447,362 & 0.9085 & 0.7613 \\  & & & (0.0029) & (0.0742) \\ \multirow{2}{*}{SegNet} & \multirow{2}{*}{RGB + NIR} & 29,444,742 & 0.7833 & 0.1856 \\  & & & (0.0155) & (0.0537) \\ \hline \multirow{2}{*}{DeepLab V3+} & \multirow{2}{*}{RGB+RE+NIR} & 54,701,010 & 0.8923 & 0.5700 \\  & & & (0.0045) & (0.1291) \\ \multirow{2}{*}{U-Net} & \multirow{2}{*}{RGB+RE+NIR} & 40,447,938 & 0.8878 & 0.5312 \\  & & & (0.0120) & (0.0864) \\ \multirow{2}{*}{SegNet} & \multirow{2}{*}{RGB+RE+NIR} & 29,445,318 & 0.8136 & 0.4829 \\  & & & (0.0301) & (0.1887) \\ \hline \hline \end{tabular}
\end{table}