Prediction of Soil Properties Using Quantile Regression Forest Machine Learning Algorithm – A Case Study of Salem and Rasipuram Block, Tamil Nadu, India

Abstract

Digital soil mapping is a growing technology for mapping soil properties instead of conventional soil mapping. Especially for what are all countries have large geographical areas and human, not accessible areas. Compared to conventional soil mapping it is cost-wise less and more accurate. At the world level, globalsoilmap.net has taken the initiative for creating digital soil maps. In India like countries very much needed for digital soil mapping, is essential for agricultural planning, and decision-makers decide on it.  This study predicted the soil properties such as sand, silt, clay, pH, and OC using the Quantile Regression Forest machine learning algorithm also provides uncertainty. The main aim of this study was to predict the soil properties in the top two depth intervals such as surface and subsurface. For achieving this goal, 56 soil samples were collected across the study area, and many environmental covariates were used for that such as DEM derivatives, satellite imagery, and Climatic Data. This study, using 56 soil samples data taken from the traditional soil survey, is a limited number of soil samples this tried to achieve a higher accuracy result using QRF.