# This file contains metadata for the Best Fit Interpolator QGIS plugin.

[general]
name=Best Fit Interpolator
qgisMinimumVersion=3.14
qgisMaximumVersion=4.99
description=Decision-support plugin for selecting, validating, and applying spatial interpolation methods in QGIS.
version=2.1
author=Laura Delgado Bejarano and Lucas Rios do Amaral
email=ladelgadobe@unal.edu.co
linkedin_laura=https://www.linkedin.com/in/laura-delgado-bejarano-09b6681a2/
linkedin_lucas=https://www.linkedin.com/in/lucas-rios-do-amaral-bb302449/

about=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: ladelgadobe@unal.edu.co. 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.
changelog=Version 2.1: Security correction: shared bounded Designer XML reader rejects DTDs and entity references without additional dependencies; explicit variogram model names remove scanner ambiguity while preserving all numerical formulas and search defaults. Offline English, Spanish and Brazilian Portuguese interface with a saved language selector, translated dialogs, plots and new reports. Ordinary and linear Universal Kriging with conditional SD/variance maps; joint constrained weighted least-squares MoM fitting and explicit model-scale/practical-range conventions. RF tree-dispersion maps and RK conditional residual-kriging uncertainty, with an optional Add SD layer control for standalone and Framework runs. Automatic bounded RF/SVM/RK grid search in Framework using the individual workflows' routines and defaults, configurable budgets, exact-input cache reuse and report provenance. Independent Framework and standalone maps, models, metrics and SD choices; Framework previews stay empty until a successful Framework interpolation. Independent raster-file predictor readers, coverage accounting, responsive validation with cancellation and hidden Windows helper processes. Reports distinguish initial recommendations, validation winners, next selections and actual executions, and display the complete map comparison at the report width. Updated 51-page English manual, retaining its stable filename and the original Auto, LOOCV and K-Fold validation workflow. Version 2.0 introduced outlier diagnostics, map comparison, reports, dense/massive processing and QGIS 3/4 compatibility.

tracker=https://github.com/ladelgadobe/BestFitInterpolation/issues
repository=https://github.com/ladelgadobe/BestFitInterpolation
manual=https://github.com/ladelgadobe/BestFitInterpolation/blob/main/BestFitInterpolator_User_Manual.pdf
article=https://link.springer.com/article/10.1007/s11119-025-10311-8
article_title=Performance of interpolation methods in digital soil mapping: the influence of data characteristics
article_citation=Laura Delgado Bejarano, Agda Loureiro Gonçalves Oliveira, João Vitor Fiolo Pozzuto, Dario Castañeda Sánchez, and Lucas Rios do Amaral (2026). Performance of interpolation methods in digital soil mapping: the influence of data characteristics. Precision Agriculture, 27, Article 10. https://doi.org/10.1007/s11119-025-10311-8

hasProcessingProvider=False
tags=interpolation, geostatistics, kriging, IDW, TPS, machine learning, digital soil mapping, precision agriculture, QGIS
homepage=https://github.com/ladelgadobe/BestFitInterpolation
category=Raster
icon=icon.png
experimental=False
deprecated=False
server=False
