Data/hora: 08/06/2024 18:18:53

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