## 4 Discussion  
In this study, a CNN model was adopted as a method to predict WC and \(\rho_{dry}\) using soil surface images. Study results showed that the CNN model successfully performed the training and could predict WC and \(\rho_{dry}\) through soil surface images. The segmented images came in three sizes: 216 \(\times\) 216, 432 \(\times\) 432, and 864 \(\times\) 864 pixels, which represented 3.9 \(\times\) 3.9, 7.8 \(\times\) 7.8, and 15.6 \(\times\) 15.6 mm\({}^{2}\), respectively. Because color differences due to segmented image size were not large, the prediction of WC became more accurate the larger the amount of training data. Even in the case of \(\rho_{dry}\) prediction, the overall accuracy was high if there was a lot of training data, but it was not overwhelmingly superior as in the WC model. In the case of soil texture of L and SiL, the accuracy of the 864 \(\times\) 864 resolution model was the best, and in the case of soil texture of L and SiCL, the accuracy of the 216 \(\times\) 216 resolution model was the highest. This was expected because the difference in void distribution between segmented images was larger than that of color. According to [PERSON]'s [34] pore-size classification, soil pores are divided into macropores, mesopores, micropores, etc. Macropores are pores with an equivalent diameter of 0.075 mm or greater. Macropores are pores in which crop roots can easily grow and are those that are most significantly reduced by compaction [35,36]. Thus, macropores have the highest correlation with soil dry density. Macropores are divided into subclasses according to size, such as coarse (>5 mm), medium (2-5 mm), fine (1-2 mm), and very fine (0.075-1 mm). By comparing the size of macropores and segmented images, those segmented images with a 216 \(\times\) 216 resolution can be represented on the image up to fine macropores. However, the \(\rho_{dry}\) prediction model of 216 \(\times\) 216 resolution showed a higher overall performance than other models did. This was because the 216 \(\times\) 216 model had four times more training data than the 432 \(\times\) 432 model and 16 times more than the 864 \(\times\) 864 model did. Thus, although the 216 \(\times\) 216 model was the most accurate in the current results, it is expected that the accuracy of other models would become better with additional training data. Furthermore, as the size of the segmented image increases, the model is expected to become more general, as more features of pore distribution can be included in the image. We examined whether a general CNN algorithm could learn the features of soil surface images and consequently predict soil properties. As new deep learning algorithms are continuously being developed, the accuracy of soil image analysis and feature prediction techniques using deep learning is predicted to increase. However, as in the results of this study, the amount of training data has a great influence on accuracy. Most surface images of soil on the internet cannot be used as training data because the shooting conditions are different and there are no data on soil characteristics. Therefore, securing a good soil image database for deep learning is considered a priority for future research. We proposed a CNN model that can predict soil moisture and soil dry density with a soil surface image. The advantage of the proposed method is that it is accessible because it uses a commercial-level camera, which is currently the most widespread sensor. Addition  
Figure 13: Quantitative results of soil dry density prediction by the convolutional neural network model. ally, there is no need to purchase a specific purpose sensor. This can be a great strength in the field of agricultural land and field-scale water resource management. The results of this study enable detailed agricultural land observation compared to remote sensing, which aims to observe large areas. However, it is difficult to manually acquire surface soil images of many points of interest.