Data/hora: 23/09/2024 03:08:44

Biblioteca(s): 	Embrapa Arroz e Feijão.	
Tipo da produção científica: 	Artigo em Periódico Indexado	
Circulação/Nível: 	A - 1	
Data corrente: 	23/04/2019	
Data da última atualização: 	12/02/2020	
Autoria: 	MORAIS, P. A. de O.; SOUZA, D. M. de; MADARI, B. E.; SOARES, A. da S.; OLIVEIRA, A. E. de.	
Afiliação: 	PEDRO AUGUSTO DE OLIVEIRA MORAIS, UFG; DIEGO MENDES DE SOUZA, CNPAF; BEATA EMOKE MADARI, CNPAF; ANDERSON DA SILVA SOARES, UFG; ANSELMO ELCANA DE OLIVEIRA, UFG.	
Título: 	Using image analysis to estimate the soil organic carbon content.	
Ano de publicação: 	2019	
Fonte/Imprenta: 	Microchemical Journal, v. 147, p. 775-781, June 2019.	
ISSN: 	0026-265X	
DOI: 	10.1016/j.microc.2019.03.070	
Idioma: 	Inglês	
Conteúdo: 	Soil test for organic carbon content using the Walkley-Black standard method is a laborious task that takes almost 5 working days to report the results. In order to improve lab analysis efficiency, a new analytical method using digital images is proposed. Using multivariate image analysis (MIA) soil organic carbon (SOC) contents can be determined in<3 days. The MIA method for SOC was developed using 177 soil samples collected from 3 regions of Brazil (North, West Central, and Northeast). Digital images of soil samples were correlated with the organic carbon contents determined by the Walkley-Black standard method using multivariate regression algorithms. In the end, MIA model employing a machine learn approach using least squares support vector machine (LS-SVM), presented an excellent correlation, r2 > 0.93, between image data and SOC contents measured by the standard analytical method. The proposed MIA method is eco-friendly, cheap, fast, and a clean alternative that can be employed by soil testing laboratories for measuring SOC contents.	
Thesagro: 	Carbono; Solo Orgânico.	
Thesaurus Nal: 	Chemometrics; Green chemistry; Soil test values.	
Categoria do assunto: 	P Recursos Naturais, Ciências Ambientais e da Terra	
Registro original: 	Embrapa Arroz e Feijão (CNPAF)	
Exemplares: 	CNPAF - (35514-1) - UPC - AP - DD - 2019 -  - 2019 - DIS