<i>Smart-Map</i>: An Open-Source QGIS Plugin for Digital Mapping Using Machine Learning Techniques and Ordinary Kriging

Abstract

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 Smart-Map 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 Support Vector Machine (SVM) algorithm were implemented. The SVM model can use vector and raster layers available in QGIS as covariates at the time of interpolation. Covariates in the SVM model were selected based on spatial correlation measured by Moran’s Index (I’Moran). To evaluate the performance of the Smart-Map 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 SVM were performed for sampling grids with 38, 75, and 112 sampled points. R2 and RMSE were used to evaluate the performance of the methods. SVM 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 R2 values ranging from 0.05 to 0.83 and RMSE ranging from 0.07 to 12.01 were predicted by the methods tested.

Keywords

• precision agriculture
• geographic information systems (GIS)
• geoprocessing
• artificial intelligence
• soil mapping