Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/55209
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dc.creatorSwetha, R. K.-
dc.creatorDasgupta, Subhadip-
dc.creatorChakraborty, Somsubhra-
dc.creatorLi, Bin-
dc.creatorWeindorf, David C.-
dc.creatorMancini, Marcelo-
dc.creatorSilva, Sérgio Henrique Godinho-
dc.creatorRibeiro, Bruno Teixeira-
dc.creatorCuri, Nilton-
dc.creatorRay, Deb Prasad-
dc.date.accessioned2022-09-26T22:39:32Z-
dc.date.available2022-09-26T22:39:32Z-
dc.date.issued2022-08-
dc.identifier.citationSWETHA, R. K. et al. Using Nix color sensor and Munsell soil color variables to classify contrasting soil types and predict soil organic carbon in Eastern India. Computers and Electronics in Agriculture, [S. I.], v. 199, 107192, Aug. 2022. DOI: https://doi.org/10.1016/j.compag.2022.107192.pt_BR
dc.identifier.urihttps://doi.org/10.1016/j.compag.2022.107192pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/55209-
dc.description.abstractOptimal soil management depends on rapid and frequent monitoring of key soil properties, which are conventionally measured in the laboratory using laborious wet-chemistry protocols. The Nix color sensor has recently exhibited promise for predicting several soil properties using soil color. This study evaluated the relationship between the Munsell Soil Color Chart (MSCC) color values of dry and ground surface soil samples to those reported by the Nix color sensor with (NixSTD) and without MSCC standardization (NixNON-STD) to classify 371 samples collected from three contrasting soil types, collected from three agro-climatic zones (coastal saline zone, red and laterite zone, and Gangetic alluvial zone) and to predict soil organic carbon (OC) using different multivariate data mining algorithms. Comparing the CIEL*a*b* color values reported by the MSCC and the NixSTD, an acceptable mean color difference (ΔE*ab) value of 5.20 was obtained, indicating the potential accuracy of the Nix sensor. Principal component analysis efficiently clustered the soil types using the RGB variables extracted from the MSCC color chips in tandem with the NixSTD/NixNON-STD data. Both classification tree and linear support vector machine algorithms perfectly classified all three contrasting soil types using NixNON-STD data alone. Besides, the combination of the MSCC and the NixNON-STD datasets produced the best OC prediction (R2 = 0.66) via random forest (RF) algorithm and indicated the potential of Nix in digital soil morphometrics. In most of the RF models, redness (a*), yellowness (b*), and yellow (Y) variables appeared influential, presumably because of their negative correlation with OC in red and laterite soils. More research is warranted to measure the impacts of variable soil moisture and other confounding soil morphological features on the soil classification and OC prediction performance to extend the approach for classifying soil types and predicting OC in-situ.pt_BR
dc.languageenpt_BR
dc.publisherElsevierpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceComputers and Electronics in Agriculturept_BR
dc.subjectSoil colorpt_BR
dc.subjectSoil organic carbonpt_BR
dc.subjectNixpt_BR
dc.subjectMunsell soil color chartpt_BR
dc.subjectRandom forestpt_BR
dc.subjectSolo - Corpt_BR
dc.subjectSolo - Carbono orgânicopt_BR
dc.subjectCarta de Munsellpt_BR
dc.subjectFloresta Aleatóriapt_BR
dc.titleUsing Nix color sensor and Munsell soil color variables to classify contrasting soil types and predict soil organic carbon in Eastern Indiapt_BR
dc.typeArtigopt_BR
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