Quantifying soil organic matter for sustainable agricultural land management with soil color and machine learning technique

Abstract

Abstract As interest in sustainable agricultural land management continues to grow, there is a need for advanced techniques that enable easy and rapid quantification of soil characteristics. Soil organic matter (SOM) is a critical factor in determining soil health. Unfortunately, contemporary techniques for SOM content analysis are laborious, time consuming, and resource intensive. In response to this challenge, our study has developed a statistical model for forecasting the SOM content using soil color indices and machine learning algorithms. Color indices, including brightness, hue, and saturation, were derived from the soil images captured by a smartphone. The correlation between color indices and SOM reveals the negative correlations between brightness (−0.790) and hue (−0.420) with the SOM content at significance level ( p ) <0.01. Conversely, the saturation of soil color, which performed best with the predictive model, exhibited a positive relationship (+0.120, p <0.05). The predictive performance of our model outperformed random forest (RF) algorithm compared to both multiple linear regression (MLR) and support vector machine (SVM). The RF algorithm achieved the highest coefficient of determination ( R 2 ) value of 0.984. Furthermore, it demonstrated the lowest error metrics. Notably, the root mean squared error value for the RF algorithm was only 0.025% with the training dataset, whereas the MLR and SVM algorithms yielded relatively higher values at 0.029% and 0.110%, respectively. These findings highlight the presence of a nonlinear relationship in predicting the SOM content, which the RF algorithm effectively captures. This approach offers accurate predictions of the SOM content, supporting sustainable agricultural land management through rapid and easy quantification.