## 6 Discussion  
In this study, an equation of predicted water content was developed through an experiment to obtain actual water content and hyperspectral information. Therefore, hyperspectral information was converted to water content and displayed as a CCM. The CCM was intended to be used for quality control in the area of subgrade compaction during the road construction process. The final discussion points are as follows:  
1. In the process of acquiring hyperspectral information using a drone, data must be secured by flying the same route at least twice, as when using the push-broom method, distortion occurs in the shot due to the influence of wind, among other things. Figure 17: Utilization of CCM through Google Earth: (**a**) Normal orthophoto; (**b**) Water content separated into 2.5% units; (**c**) Classification based on optimal water content; (**d**) Area of dust scattering. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Water Content** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline CCM (\%) & 4.75 & 3.87 & 9.24 & 8.74 & 6.45 & 5.21 & 4.98 & 2.21 & 1.87 & 2.57 \\ Measuring (\%) & 4.45 & 3.94 & 9.39 & 7.54 & 5.42 & 4.18 & 2.84 & 1.95 & 2.21 & 3.58 \\ R\({}^{2}\) & & & & & & 0.872 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of CCM data and measured water content. Moreover, hyperspectral cameras and drones that receive coordinates may have differences in coordinate reading depending on the surrounding Internet environment. Since the measured hyperspectral information has a constant value regardless of this, there is no problem with the final CCM, but it is recommended to simply select a visually clear image to produce the CCM. 2. The surface and inside water content require a high function ratio range and a precise data acquisition environment. The current measured data have the disadvantage of lacking information on the high water-content section (above 10%) because the research was conducted in the summer (6-8 months). Accordingly, it is necessary to secure data on high water content by artificially creating a high-water content section or by taking pictures immediately after rain. In addition, there were 100 actual water data points acquired during one drone flight, and given that it takes approximately 1 to 1.5 h, there is a possibility that an instantaneous change of water content may occur. In future research, we believe it will be necessary to immediately measure the water content, and the number of measurements should be reduced from the existing 100 times to 10-20 times. 3. There is a need to supplement machine learning with additional and precisely measured water content data. The current development equation is being developed as MLR based on the extraction of 10 independent variables, and information on the number of variables extracted is omitted from this study. In future research, it is necessary to analyze reliability according to the number of independent variables extracted and consider various models such as polynomial regression in addition to MLR. In addition, we have determined that reliability analysis will need to be undertaken from various perspectives, rather than simply R2. 4. This study aims to improve water-content measurement methods for quality control during subgrade compaction performed during road construction. Therefore, the CCM of water content must be delivered to the operator as quickly as possible. At the current level of technology, it is impossible to capture images with a drone and create a CCM at the same time. For CCM production, CCM follow-up processing is performed on a separate PC or laptop after the drone's flight is completed and it has landed.