Identification of soil texture and color using machine learning algorithms and satellite imagery.

The demand for high-quality and cost-effective soil information is increasing due to its importance in land-use planning and precision agriculture. This study aimed to estimate soil texture and color using satellite imagery as input variables for support vector regression (SVR) and decision tree regression (DTR) models. Soil properties, including soil texture (clay, silt, and sand) and color components (Hue, Value, and Chroma), were measured. Additionally, a wide range of indices derived from MODIS sensor imagery were calculated. Duncan's test at a 5% significance level revealed significant temporal differences among the indices, although no significant differences were observed in the mean indices concerning soil texture variability. The results of error metrics, including root mean squared error (RMSE), absolute mean absolute percentage error (AMAPE), mean absolute error (MAE), mean squared error (MSE), and ratio of performance to deviation (RPD), demonstrated the superiority of the SVR method over the DTR method. Soil texture classification using the soil texture triangle and validation methods showed good agreement between measured and predicted data using the SVR approach. The lowest RMSE was observed for Hue, indicating the most accurate prediction, whereas sand showed the highest error. The differences in error metrics, including RMSE, AMAPE, MAE, MSE, and RPD, between SVR and DTR methods were 0, 0.2, 0, 0, and 0.8 for Hue and 0.41, 5, 0.1, 0.1, and 0.87 for sand, respectively. For future research, it is recommended to explore the combination of SVR with optimization techniques such as genetic algorithms to further improve the accuracy of soil texture and color predictions.