Soil subgroup prediction via portable X-ray fluorescence and visible near-infrared spectroscopy

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorBenedet, Lucas-
Autor(es): dc.creatorFaria, Wilson Missina-
Autor(es): dc.creatorSilva, Sérgio Henrique Godinho-
Autor(es): dc.creatorMancini, Marcelo-
Autor(es): dc.creatorGuilherme, Luiz Roberto Guimarães-
Autor(es): dc.creatorDemattê, José Alexandre Melo-
Autor(es): dc.creatorCuri, Nilton-
Data de aceite: dc.date.accessioned2026-02-09T11:52:52Z-
Data de disponibilização: dc.date.available2026-02-09T11:52:52Z-
Data de envio: dc.date.issued2020-09-11-
Data de envio: dc.date.issued2020-09-11-
Data de envio: dc.date.issued2020-04-15-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/43017-
Fonte completa do material: dc.identifierhttps://www.sciencedirect.com/science/article/abs/pii/S0016706119324826#!-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1149503-
Descrição: dc.descriptionRecently, portable X-ray fluorescence (pXRF) spectrometer and visible near-infrared (Vis-NIR) spectroscopy are increasingly being applied for soil types and attributes prediction, but a few works have used them combined in tropical regions. Thus, this work aimed at analyzing models’ performance when predicting soil types at subgroup taxonomic level via pXRF and Vis-NIR separately and together. 315 soil samples were collected in both A and B horizons in three important Brazilian states. Samples undergone laboratorial analyses for soil classification and were submitted to pXRF and Vis-NIR (350–2500 nm) analyses. Vis-NIR spectral data preprocessing was evaluated utilizing Savitzky-Golay (WT) and Savitzky-Golay with Binning (WB) methods. Four classification algorithms were employed in modeling: Support Vector Machine with Linear (SVM-L) and Radial (SVM-R) kernel, C5.0, and Random Forest (RF). Predictions were made using only B horizon and using A + B horizon data. Overall accuracy and Cohen’s Kappa index evaluated model quality. Both sensors displayed efficacy in soil types prediction. A + B horizons data combined using pXRF + Vis-NIR via SVM-R (WT and WB) delivered accurate predictions (89.32% overall accuracy and 0.75 Kappa index), but the best predictions were achieved using only B horizon data via pXRF with RF, pXRF + Vis-NIR (WT) with RF, pXRF + Vis-NIR (WB) with C5.0, and pXRF + Vis-NIR (WB) with RF (89.23% overall accuracy and 0.80 Kappa index). For tropical soils, soil subgroup prediction using only B horizon data obtained by pXRF in tandem with RF algorithm may be a viable alternative to assist in soil classification, especially when the acquisition of Vis-NIR is not possible.-
Idioma: dc.languageen-
Publicador: dc.publisherElsevier-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceGeoderma-
Palavras-chave: dc.subjectSoil classification-
Palavras-chave: dc.subjectSupport vector machine-
Palavras-chave: dc.subjectTropical soils-
Palavras-chave: dc.subjectProximal sensors-
Palavras-chave: dc.subjectPortable X-ray fluorescence (pXRF)-
Palavras-chave: dc.subjectClassificação do solo-
Palavras-chave: dc.subjectMáquina de vetor de suporte-
Palavras-chave: dc.subjectSensores proximais-
Palavras-chave: dc.subjectFluorescência de raios-x portátil-
Título: dc.titleSoil subgroup prediction via portable X-ray fluorescence and visible near-infrared spectroscopy-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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