Best Fit Interpolator
Decision-support plugin for selecting, validating, and applying spatial interpolation methods in QGIS.
Best Fit Interpolator supports spatial interpolation workflows for environmental, soil, and precision-agriculture data. The plugin compares deterministic, geostatistical, machine-learning, and hybrid approaches, including IDW, TPS, ordinary and linear universal kriging, MoM/REML fitting, Random Forest, SVM, and Regression Kriging. It provides data diagnostics, semivariogram visualization, validation metrics, observed-vs-predicted plots, interpolation maps, and a framework-guided decision workflow based on: Delgado Bejarano, L., Loureiro Goncalves Oliveira, A., Fiolo Pozzuto, J. V., Castaneda Sanchez, D., and Rios do Amaral, L. (2026). Performance of interpolation methods in digital soil mapping: the influence of data characteristics. Precision Agriculture, 27(1), 10. https://doi.org/10.1007/s11119-025-10311-8. Contact: [email protected]. LinkedIn: Laura Delgado Bejarano - https://www.linkedin.com/in/laura-delgado-bejarano-09b6681a2/; Lucas Rios do Amaral - https://www.linkedin.com/in/lucas-rios-do-amaral-bb302449/. Additional external Python dependencies are required for some methods, including NumPy, SciPy, pandas and scikit-learn. When compatible packages are unavailable, the plugin requests permission to install them into its local _deps directory; this requires internet access but no administrator rights. Interface translations are bundled for English, Spanish and Brazilian Portuguese.