Data/hora: 12/06/2024 13:53:19

Provedor de dados: 	Revista Brasileira de Engenharia Agrícola e Ambiental	
País: 	Brazil	
Título: 	Proposal of automated computational method to support Virginia tobacco classification	
Autores: 	Tedesco,Leonel P. C.; Freitas,Adriano da C. de; Molz,Rolf F.; Schreiber,Jacques N. C.	
Data: 	2019-10-01	
Ano: 	2019	
Palavras-chave: 	Image processing; Partial least square; Artificial neural network.	
Resumo: 	ABSTRACT This article proposes an automatic method for classification of cured tobacco leaves. Typically this process is performed manually, allowing the occurrence of human errors. In addition, the existence of an automated comparative procedure, helping to perform the classification, can make this process faster and more transparent. In order to implement the method, non-invasive to the agricultural product, 250 samples of Virginia tobacco digital images in the RGB and HSV color models were analyzed. The validation of the method was carried out using partial least squares (PLS) and artificial neural network (ANN), presenting a qualitative and quantitative analysis of both tools. It has been verified that the PLS can be applied to this method, as it has a shorter computational time, better suiting a real-time process. It can be verified that the ANN obtained better prediction results. Both methods employed had better results when adopting the RGB color model, reaching coefficient of determinations of 68 and 96% for the PLS and ANN methods, respectively.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662019001000782	
Editor: 	Departamento de Engenharia Agrícola  - UFCG	
Relação: 	10.1590/1807-1929/agriambi.v23n10p782-786	
Formato: 	text/html	
Fonte: 	Revista Brasileira de Engenharia Agrícola e Ambiental  v.23 n.10 2019	
Fonte: 	info:eu-repo/semantics/openAccess