## 4 Discussion  
This study aimed to predict the WC and BD through a DNN regression model, using features extracted from in situ soil surface images. The DNN regression model outperformed the multiple regression analysis, even when obstacles other than soil were present in the in situ soil surface images. This model predicted the WC and BD with high accuracy and demonstrated remarkable performance, even when learning soils of different textures together. The significance of this study lies in its applicability in in situ agricultural land, where the prediction of surface WC and BD can help in understanding how soil affects the circulation of water resources. This is particularly important given the recent changes in climate, which necessitate the more detailed management of soil moisture in agricultural fields. Therefore, the findings of this study are expected to be highly relevant in this regard. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Input Variables**} & \multirow{2}{*}{**Output Variable**} & \multirow{2}{*}{**Dataset**} & \multicolumn{6}{c}{**Metrics**} \\ \cline{4-9}  & & & **MAE** & **MAPE** & \(R\) & \(R^{2}\) & **MSE** & **RMSE** \\ \hline Hue & & & & & & & & \\ Sat & & & & & & & & \\ Value & & & & & & & \\ Gray & & B & 1.13 & 11.4 & 0.97 & 0.94 & 2.43 & 1.56 \\ Skewness & & C & 0.92 & 6.5 & 0.97 & 0.93 & 1.29 & 1.14 \\ Kurtosis & & A, B, C & 1.23 & 16.9 & 0.97 & 0.94 & 2.32 & 1.52 \\ \cline{2-9} Dissimilarity & & A & 0.36 & 3.6 & 0.95 & 0.91 & 0.20 & 0.45 \\ \cline{2-9} Emergency & & B & 0.43 & 4.1 & 0.95 & 0.90 & 0.26 & 0.51 \\ \cline{2-9} Correlation & Bulk density (kN/m\({}^{3}\)) & C & 0.53 & 4.3 & 0.96 & 0.93 & 0.43 & 0.66 \\ \cline{2-9} H0, H1, H2, H3 & A, B, C & 0.63 & 5.7 & 0.91 & 0.83 & 0.61 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Result of deep neural network regression models using features from soil surface images. Figure 7: Prediction result of bulk density using features from soil surface images produced by multiple regression analysis and the deep neural network regression model. This study focused on soils of three textures: SiL, SL, and LS, which are widely distributed in agricultural land [40; 41; 42; 43]. These soils have a high content of sand or silt, with a clay content of less than about 27% [1]. However, clay has a higher cohesive strength and water holding capacity than sand or silt due to their small particle size and high surface area [44]. Therefore, the color, texture, and shape features of clayey soil surface images may differ from those of the soil samples used in this study. Thus, further research is necessary to develop a more general model by adding soil textures with a high clay content. Furthermore, the camera system used in this study has the potential to be combined with agricultural and construction machinery, enabling the easy observation of a wide area. Currently, construction equipment such as tractors, dozens, and compactors are equipped with various sensors. By combining the findings of this study with such equipment, it is anticipated that they will significantly aid in soil management at large-scale sites. Specifically, by integrating this system with a compactor at a construction site where compaction management is essential, it can play a vital role in managing the optimal moisture content and maximum dry density required for proper compaction. Since the properties of soil can vary greatly depending on the texture, it is considered necessary to construct new training data when applying the results of this study to soils with different textures. Furthermore, as the behavior of soil may differ even among those of the same texture due to the sand, silt, and clay content, calibration becomes essential when applying the outcome of this research. If comprehensive databases on soil surface images, including those from diverse locations and textures, are secured in the future, the applicability of this study is expected to increase significantly.