Using Machine Learning Algorithms to Mapping of the Soil Macronutrient

Fine resolution spatial digital maps of soil macronutrients, which are an important factor in plant nutrition, are needed to support agricultural productivity. Digital soil maps obtained with high precision and accuracy are at the forefront of innovative technological initiatives to increase agricultural production. We had 91 topsoil observations, indices produced from satellite imagery, topographical variables produced from the DEM, and the CORINE land cover classes map which showed the effectiveness of agricultural activities for many years. Our first ultimate goal was to create digital soil maps with a spatial resolution of 30 m of various soil macronutrients (P, Ca, Mg, K). We compared three machine learning algorithms: multiple linear regression, support vector machine, and random forest algorithms.