Artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepper
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.
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2016
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oai:scielo:S1415-436620160006005072016-06-03Artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepperPandorfi,HélitonBezerra,Alan C.Atarassi,Roberto T.Vieira,Frederico M. C.Barbosa Filho,José A. D.Guiselini,Cristiane microclimate sweet pepper expert system computational vision 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.info:eu-repo/semantics/openAccessDepartamento de Engenharia Agrícola - UFCGRevista Brasileira de Engenharia Agrícola e Ambiental v.20 n.6 20162016-06-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662016000600507en10.1590/1807-1929/agriambi.v20n6p507-512 |
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Pandorfi,Héliton Bezerra,Alan C. Atarassi,Roberto T. Vieira,Frederico M. C. Barbosa Filho,José A. D. Guiselini,Cristiane |
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Pandorfi,Héliton Bezerra,Alan C. Atarassi,Roberto T. Vieira,Frederico M. C. Barbosa Filho,José A. D. Guiselini,Cristiane Artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepper |
| author_facet |
Pandorfi,Héliton Bezerra,Alan C. Atarassi,Roberto T. Vieira,Frederico M. C. Barbosa Filho,José A. D. Guiselini,Cristiane |
| author_sort |
Pandorfi,Héliton |
| title |
Artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepper |
| title_short |
Artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepper |
| title_full |
Artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepper |
| title_fullStr |
Artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepper |
| title_full_unstemmed |
Artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepper |
| title_sort |
artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepper |
| description |
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. |
| publisher |
Departamento de Engenharia Agrícola - UFCG |
| publishDate |
2016 |
| url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662016000600507 |
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