## 3 Results  
### Quality of Original and Segmented Images  
Figure 4 shows the gray value according to the soil conditions of 240 original images. As WC gradually increased, the gray value tended to decrease overall. When WC was high, the gray value was maintained or partially increased. This was because as soil was almost saturated, subsequent moisture came out to the soil surface and reflected light [26]. As \(\rho_{dry}\) increased, the gray value appeared to increase. This was because the distribution of voids, which are relatively dark regions, decreased as the soil was compacted [27]. This tendency was maintained well in SL, L, and SiL with high sand content. SiCL with high clay content was found to have no significant difference in gray value change with increasing \(\rho_{dry}\) under low WC conditions. Sample segmented images for WC and \(\rho_{dry}\) prediction are shown in Figures 5 and 6. As WC increased, the brightness of the image decreased and the saturation increased. Additionally, the higher the WC, the more the soil became agglomerated. As \(\rho_{dry}\) increased, the distribution of voids in the surface layer decreased. No significant color difference according to the resolution of the images was observed. Figure 4: Gray value of original soil surface images according to soil texture, soil water content and soil dry density. Figure 7 shows the histogram of the sample original image and the histogram of the segmented image by resolution. There were no significant differences in the peak and shape between the histograms of the original and segmented images. Therefore, the segmented images represented the color distribution of the original image well. However, outliers were observed when the resolution of the segmented images was small. Figure 5: Sample segmented soil surface images under different soil water content conditions. Each image is a cropped image with 3 different sizes from the original soil surface image with a resolution of \(5184\times 3456\) pixels. Soil texture is sandy loam. Figure 6: Sample segmented soil surface images under different soil dry density conditions. Each image is a cropped image with 3 different sizes from the original soil surface image with resolution of \(5184\times 3456\) pixels. Soil texture is loam. Figure 8 shows the distribution of the average gray value of the original and segmented images. The smaller the segment size, the higher the variance of the gray value, and the more outliers. As shown in Figure 4, the color of the soil was observed differently depending on WC and \(\rho_{dry}\). Therefore, if the variance of image color increased, the prediction of WC and \(\rho_{dry}\) may have been adversely affected. However, since a CNN model learns by finding the features of the image by itself, it may succeed in predicting based on other features of the image as well as soil color. Figure 8: Distribution of average gray value of soil surface images. The solid red line is the average gray value of the original image, and the number of segmented images for each resolution was 384, 96, and 24, respectively. Figure 7: Gray value histogram of soil surface images. The solid red line is a histogram of the original soil surface image, and the solid grayscale lines are histograms of segmented images. N is the number of segmented images and its histograms. Figure 9 shows the difference of void distribution among the segmented images. The distribution of voids is a factor closely related to soil dry density. The original image used to analyze the void distribution in Figure 9 had a texture of SL and a soil condition of 25% WC and \(\rho_{dry}\) of 1.3 t/m\({}^{3}\). To confirm the void distribution of the original image, the original image was converted to grayscale, and thresholding was performed with the Otsu thresholding method to obtain a binary image divided into soil solid and void. In the binary image, it was assumed that white represented soil solid and black represented a void. In the original image, 37% of the entire area was evaluated as a void.