# Enhancing Density Prediction of Agricultural Land Soil through Void Area Curve Analysis
## 4 Discussion
### Practical Considerations and Limitations  
In this study, we proposed the VAC from a soil surface image that can be used to predict the \(\rho_{d}\) in agricultural lands. The limitations of this study are summarized as follows. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Method**} & \multicolumn{2}{c}{**RMSE (t/m\({}^{3}\))**} & \multicolumn{2}{c}{**Range of Water Content (\%)**} & \multicolumn{2}{c}{**Number of Tests**} & \multicolumn{1}{c}{**Texture**} \\ \hline \multirow{3}{*}{Field density test [6]} & Sand Cone & 0.025 & \multirow{3}{*}{8.3 to 13.3} & 49 & \multirow{3}{*}{Sandy loam} \\ \cline{2-2} \cline{6-6}  & Nuclear & 0.042 & & 253 & & Sandy loam \\ \cline{2-2} \cline{6-6}  & Drive Cylinder & 0.057 & & 60 & \\ \hline \multirow{3}{*}{[PERSON] et al. [28]} & Image analysis using & \multirow{3}{*}{0.044 to 0.107} & \multirow{3}{*}{5 to 25} & \multirow{3}{*}{80} & Sandy loam, \\  & convolutional neural network & & & & & Samy loam \\ \cline{1-1} \cline{3-6}  & Image analysis using & \multirow{3}{*}{0.080} & \multirow{3}{*}{1.2 to 22.3} & \multirow{3}{*}{74} & Loamy sand, \\  & deep neural network & & & & & Samy loam \\ \hline \multirow{3}{*}{This study} & Image analysis using & \multirow{3}{*}{0.037} & \multirow{3}{*}{5 to 25} & \multirow{3}  
Four soil textures, SL, L, SiL, and SiCL, were analyzed, ranging from SL with a high sand content to SiCL with a high clay and silt content, but the prediction performance may vary in soils with different textures. In particular, it is considered that the accuracy may decrease in soil textures such as sandy clay, silty clay, and clay where the clay content is higher than SiCL. This is because macropores may not be well represented at higher clay contents. It is also likely that the accuracy of the prediction will decrease if the digital image quality is poor. The histogram of the sample image presented in Figure 3b shows that the intensity of the pixels is well distributed. If the pixels were over- or under-lighted due to a camera or lighting setup mistake, the histogram would show peaks skewed towards 0 or 255. In this case, the shape of the VAC may be distorted, which will reduce its accuracy. The study was conducted on soil surface images; therefore, the data acquisition for subsurface soils was limited. Similar to the conventional in situ density test method, image analysis can be attempted after removing the upper soil; however, verification is required. In agricultural soil management, soil properties in the root zone directly affect crop growth [46]. Therefore, it is necessary to verify the applicability of the image analysis method to the subsoil region. Regarding the compaction of the sample, the entire surface layer was compacted in this study to make the surface layer even. In the soil surface layer, compaction may occur by the operation of agricultural machinery [47; 48]. In addition, it may be difficult to obtain an image when the surface layer is obstructed by vegetation or other obstacles. Therefore, for field applications, it is necessary to exclude unnecessary regions from the surface layer image. Cameras can show different colors of the same object according to settings such as color temperature and exposure. In this study, digital images were acquired under fixed lighting conditions in an indoor studio. Because the lighting conditions of the agricultural lands continuously change depending on the time of day and weather conditions, it would be desirable to install a darkroom to control the lighting conditions outside the camera. It is necessary to develop a method that can be used universally under various lighting conditions. In addition, it is necessary to review the optimal resolution of the soil surface image. Unlike other objects, soil is a material whose size affects its properties. Even with the same camera, the size of an object varies depending on the working distance. When shooting from a close distance, the soil solid and void space can be observed in more detail.