## 3 Results
### Accuracy of Soil Water Content and Soil Dry Density Prediction  
The classification accuracy of CNN model is summarized in Table 1. For WC prediction, the 216 \(\times\) 216 resolution model had the highest classification accuracy. The 432 \(\times\) 432 model  
Figure 11: Example of soil dry density prediction by the convolutional neural network model. (**a**) Classification result with a resolution of 432 \(\times\) 432 segmented images. ; (**b**) original image overlaid with classification result. Figure 10: Example of soil water content prediction by the convolutional neural network model. (**a**) Classification result with a resolution of 216 \(\times\) 216 segmented images. ; (**b**) original image overlaid with classification result. also successfully classified WC under various \(\rho_{dry}\) conditions with a total accuracy of 97.5%. The 864 \(\times\) 864 model showed total accuracy of 78.75%, which is relatively low compared to that of other models. WC is highly correlated with soil color [28; 29; 30; 31; 32; 33]. We confirmed that color differences due to resolution were not significant in the segmented images of the training set. Therefore, the 216 \(\times\) 216 resolution model with a larger training set size was considered to have the highest accuracy. Specifically, the model's strong point was its ability to predict WC with high accuracy under various soil dry density conditions. The \(\rho_{dry}\) model also showed the highest accuracy with the 216 \(\times\) 216 resolution model, similarly to the results of the WC prediction model. The resolution of each image was 0.018 mm/pixel. Thus, a 216 \(\times\) 216 pixel-segmented image represented an actual area of approximately 3.9 mm \(\times\) 3.9 mm. The CNN model was deemed to have learned the void features by itself and predicted \(\rho_{dry}\) even for small area-segmented images. The classification result of the \(\rho_{dry}\) prediction model showed relatively low classification accuracy compared to that of the WC prediction model. This was expected to appear due to the difference in the natural properties of WC and \(\rho_{dry}\). WC has a strong correlation with soil color, while \(\rho_{dry}\) is governed by the distribution of voids. There was no significant difference in soil color in the segmented images, but the distribution of voids could be prominent in some segmented images and barely appear in others. Accordingly, it was considered that the classification accuracy of \(\rho_{dry}\) was relatively low. Figures 12 and 13 show the quantitative results by the CNN models. In Figure 12, the RMSEs of the WC quantitative results were 0.18 to 0.30, 0.89 to 2.07, and 1.11 to 3.48%, respectively. Same as the classification accuracy, the accuracy of the 216 \(\times\) 216 resolution model was the highest. Compared to that of the \(216\times 216\) resolution model, the \(432\times 432\) resolution model showed a large deviation in predicted values, resulting in a relatively high RMSE. In the case of the 864 \(\times\) 864 resolution model, the RMSE increased compared to that of the 432 \(\times\) 432 resolution model, and many outliers were observed. Therefore, it was considered desirable to use a 216 \(\times\) 216 resolution model. In Figure 13, the RMSEs of the prediction results were 0.055 to 0.082, 0.054 to 0.090, and 0.044 to 0.107 \(\mathrm{t/m^{3}}\), respectively. Similar to the WC model, the accuracy of the 216 \(\times\) 216 resolution model was the highest. Although the classification accuracy of \(\rho_{dry}\) prediction was low, relatively stable prediction results were derived when RMSE was calculated by calculating quantitative values. Therefore, it was considered that it was preferable to use a quantitative result rather than a classification result for the prediction of \(\rho_{dry}\).