Machine learning-based estimation of soil organic matter using RGB values

Soil color is a straightforward yet insightful indication of soil properties. This work employed machine learning (ML) to predict soil organic matter (SOM) content utilizing 50 soil samples from Homs province, based on RGB color values. The concentration of SOM was determined using the wet oxidation method, and predictive models were built utilizing Support Vector Machine (SVM), Gaussian Process Regression (GPR), Least Squares Kernel (LSK), Ensemble Tree, Artificial Neural Network (ANN), and Multiple Linear Regression (MLR). Among these, GPR, SVM, and Ensemble Tree demonstrated robust training performance (R²=0.91, 0.89, and 0.89, respectively); however, SVM displayed decreased robustness with external data. Subsequent validation using external testing and multicriteria decision-making (MCDM) analysis demonstrated that the ANN model attained the highest predictive accuracy and generalization ability, followed by GPR. The findings indicate that machine learning models can reliably forecast soil organic matter from RGB values, providing a faster and more cost-effective substitute for laboratory analyses. This facilitates effective large-scale soil monitoring in Homs and comparable regions for improved agricultural and environmental decision-making.