| Summary: | Machine Learning (ML) algorithms have been used as an alternative to conventional and geostatistical methods in digital mapping of soil attributes. An advantage of ML algorithms is their flexibility to use various layers of information as covariates. However, ML algorithms come in many variations that can make their application by end users difficult. To fill this gap, a <i>Smart-Map</i> plugin, which complements Geographic Information System QGIS Version 3, was developed using modern artificial intelligence (AI) tools. To generate interpolated maps, Ordinary Kriging (OK) and the <i>Support Vector Machine</i> (<i>SVM</i>) algorithm were implemented. The <i>SVM</i> model can use vector and raster layers available in QGIS as covariates at the time of interpolation. Covariates in the <i>SVM</i> model were selected based on spatial correlation measured by Moran’s Index (I’Moran). To evaluate the performance of the <i>Smart-Map</i> plugin, a case study was conducted with data of soil attributes collected in an area of 75 ha, located in the central region of the state of Goiás, Brazil. Performance comparisons between OK and <i>SVM</i> were performed for sampling grids with 38, 75, and 112 sampled points. <i>R</i><sup>2</sup> and <i>RMSE</i> were used to evaluate the performance of the methods. <i>SVM</i> was found superior to OK in the prediction of soil chemical attributes at the three sample densities tested and was therefore recommended for prediction of soil attributes. In this case study, soil attributes with <i>R</i><sup>2</sup> values ranging from 0.05 to 0.83 and <i>RMSE</i> ranging from 0.07 to 12.01 were predicted by the methods tested.
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