Data/hora: 02/06/2024 13:16:12

Provedor de dados: 	91	
País: 	Brazil	
Título: 	Artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepper	
Autores: 	Pandorfi,Héliton; Bezerra,Alan C.; Atarassi,Roberto T.; Vieira,Frederico M. C.; Barbosa Filho,José A. D.; Guiselini,Cristiane.	
Data: 	2016-06-01	
Ano: 	2016	
Palavras-chave: 	Microclimate; Sweet pepper; Expert system; Computational vision.	
Resumo: 	ABSTRACT This study aimed to investigate the applicability of artificial neural networks (ANNs) in the prediction of evapotranspiration of sweet pepper cultivated in a greenhouse. The used data encompass the second crop cycle, from September 2013 to February 2014, constituting 135 days of daily meteorological data, referring to the following variables: temperature and relative air humidity, wind speed and solar radiation (input variables), as well as evapotranspiration (output variable), determined using data obtained by load-cell weighing lysimeter. The recorded data were divided into three sets for training, testing and validation. The ANN learning model recognized the evapotranspiration patterns with acceptable accuracy, with mean square error of 0.005, in comparison to the data recorded in the lysimeter, with coefficient of determination of 0.87, demonstrating the best approximation for the 4-21-1 network architecture, with multilayers, error back-propagation learning algorithm and learning rate of 0.01.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662016000600507	
Editor: 	Departamento de Engenharia Agrícola  - UFCG	
Relação: 	10.1590/1807-1929/agriambi.v20n6p507-512	
Formato: 	text/html	
Fonte: 	Revista Brasileira de Engenharia Agrícola e Ambiental  v.20 n.6 2016	
Fonte: 	info:eu-repo/semantics/openAccess