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Campo DC | Valor | Idioma |
---|---|---|
dc.creator | Benedet, Lucas | - |
dc.creator | Faria, Wilson Missina | - |
dc.creator | Silva, Sérgio Henrique Godinho | - |
dc.creator | Mancini, Marcelo | - |
dc.creator | Guilherme, Luiz Roberto Guimarães | - |
dc.creator | Demattê, José Alexandre Melo | - |
dc.creator | Curi, Nilton | - |
dc.date.accessioned | 2020-09-11T17:59:52Z | - |
dc.date.available | 2020-09-11T17:59:52Z | - |
dc.date.issued | 2020-04-15 | - |
dc.identifier.citation | BENEDET, L. et al. Soil subgroup prediction via portable X-ray fluorescence and visible near-infrared spectroscopy. Geoderma, Amsterdam, v. 365, 114212, Apr. 2020. DOI: https://doi.org/10.1016/j.geoderma.2020.114212. | pt_BR |
dc.identifier.uri | https://www.sciencedirect.com/science/article/abs/pii/S0016706119324826#! | pt_BR |
dc.identifier.uri | http://repositorio.ufla.br/jspui/handle/1/43017 | - |
dc.description.abstract | Recently, 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. | pt_BR |
dc.language | en_US | pt_BR |
dc.publisher | Elsevier | pt_BR |
dc.rights | restrictAccess | pt_BR |
dc.source | Geoderma | pt_BR |
dc.subject | Soil classification | pt_BR |
dc.subject | Support vector machine | pt_BR |
dc.subject | Tropical soils | pt_BR |
dc.subject | Proximal sensors | pt_BR |
dc.subject | Portable X-ray fluorescence (pXRF) | pt_BR |
dc.subject | Classificação do solo | pt_BR |
dc.subject | Máquina de vetor de suporte | pt_BR |
dc.subject | Sensores proximais | pt_BR |
dc.subject | Fluorescência de raios-x portátil | pt_BR |
dc.title | Soil subgroup prediction via portable X-ray fluorescence and visible near-infrared spectroscopy | pt_BR |
dc.type | Artigo | pt_BR |
Aparece nas coleções: | DCS - Artigos publicados em periódicos |
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