Development of a soil total carbon prediction model using a multiple regression analysis method

There is a need for a technology that can quickly and accurately analyze soil carbon contents. Existing soil carbon analysis methods are cumbersome in terms of professional manpower requirements, time, and cost. It is against this background that the present study leverages the soil physical properties of color and water content levels to develop a model capable of predicting the carbon content of soil sample. To predict the total carbon content of soil, the RGB values, water content of the soil, and lux levels were analyzed and used as statistical data. However, when R, G, and B with high correlations were all included in a multiple regression analysis as independent variables, a high level of multicollinearity was noted and G was thus excluded from the model. The estimates showed that the estimation coefficients for all independent variables were statistically significant at a significance level of 1%. The elastic values of R and B for the soil carbon content, which are of major interest in this study, were -2.90 and 1.47, respectively, showing that a 1% increase in the R value was correlated with a 2.90% decrease in the carbon content, whereas a 1% increase in the B value tallied with a 1.47% increase in the carbon content. Coefficient of determination (R²), root mean square error (RMSE), and mean absolute percentage error (MAPE) methods were used for regression verification, and calibration samples showed higher accuracy than the validation samples in terms of R² and MAPE.