Artificial neural networks in the classification and identification of soybean cultivars by planting region
Main Author: | |
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Publication Date: | 2011 |
Other Authors: | , , , |
Format: | Article |
Language: | eng |
Source: | Journal of the Brazilian Chemical Society (Online) |
Download full: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532011000100019 |
Summary: | Twenty soybean (Glycine max) varieties, 14 conventional and 6 transgenic varieties were analyzed for protein content, phytic acid, oil content, phytosterols, ash, minerals and fatty acids. The data were tabled and presented to the multilayer perceptron neural network for classification and identification of their planting region and whether they were a conventional or transgenic. The neural network used correctly classified and tested 100% of the samples cultivated per region. For the data bank containing information on transgenic and conventional soybean, a performance of 94.43% was obtained in the training of the neural network, 83.30% in the test and 100% in the validation. |
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Artificial neural networks in the classification and identification of soybean cultivars by planting regionmultilayer perceptron neural networksre-samplingphytosterolsfatty acidsTwenty soybean (Glycine max) varieties, 14 conventional and 6 transgenic varieties were analyzed for protein content, phytic acid, oil content, phytosterols, ash, minerals and fatty acids. The data were tabled and presented to the multilayer perceptron neural network for classification and identification of their planting region and whether they were a conventional or transgenic. The neural network used correctly classified and tested 100% of the samples cultivated per region. For the data bank containing information on transgenic and conventional soybean, a performance of 94.43% was obtained in the training of the neural network, 83.30% in the test and 100% in the validation.Sociedade Brasileira de Química2011-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532011000100019Journal of the Brazilian Chemical Society v.22 n.1 2011reponame:Journal of the Brazilian Chemical Society (Online)instname:Sociedade Brasileira de Química (SBQ)instacron:SBQ10.1590/S0103-50532011000100019info:eu-repo/semantics/openAccessGalão,Olívio F.Borsato,DionísioPinto,Jurandir P.Visentainer,Jesuí V.Carrão-Panizzi,Mercedes Concórdiaeng2011-01-24T00:00:00Zoai:scielo:S0103-50532011000100019Revistahttp://jbcs.sbq.org.brONGhttps://old.scielo.br/oai/scielo-oai.php||office@jbcs.sbq.org.br1678-47900103-5053opendoar:2011-01-24T00:00Journal of the Brazilian Chemical Society (Online) - Sociedade Brasileira de Química (SBQ)false |
dc.title.none.fl_str_mv |
Artificial neural networks in the classification and identification of soybean cultivars by planting region |
title |
Artificial neural networks in the classification and identification of soybean cultivars by planting region |
spellingShingle |
Artificial neural networks in the classification and identification of soybean cultivars by planting region Galão,Olívio F. multilayer perceptron neural networks re-sampling phytosterols fatty acids |
title_short |
Artificial neural networks in the classification and identification of soybean cultivars by planting region |
title_full |
Artificial neural networks in the classification and identification of soybean cultivars by planting region |
title_fullStr |
Artificial neural networks in the classification and identification of soybean cultivars by planting region |
title_full_unstemmed |
Artificial neural networks in the classification and identification of soybean cultivars by planting region |
title_sort |
Artificial neural networks in the classification and identification of soybean cultivars by planting region |
author |
Galão,Olívio F. |
author_facet |
Galão,Olívio F. Borsato,Dionísio Pinto,Jurandir P. Visentainer,Jesuí V. Carrão-Panizzi,Mercedes Concórdia |
author_role |
author |
author2 |
Borsato,Dionísio Pinto,Jurandir P. Visentainer,Jesuí V. Carrão-Panizzi,Mercedes Concórdia |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Galão,Olívio F. Borsato,Dionísio Pinto,Jurandir P. Visentainer,Jesuí V. Carrão-Panizzi,Mercedes Concórdia |
dc.subject.por.fl_str_mv |
multilayer perceptron neural networks re-sampling phytosterols fatty acids |
topic |
multilayer perceptron neural networks re-sampling phytosterols fatty acids |
description |
Twenty soybean (Glycine max) varieties, 14 conventional and 6 transgenic varieties were analyzed for protein content, phytic acid, oil content, phytosterols, ash, minerals and fatty acids. The data were tabled and presented to the multilayer perceptron neural network for classification and identification of their planting region and whether they were a conventional or transgenic. The neural network used correctly classified and tested 100% of the samples cultivated per region. For the data bank containing information on transgenic and conventional soybean, a performance of 94.43% was obtained in the training of the neural network, 83.30% in the test and 100% in the validation. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532011000100019 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532011000100019 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0103-50532011000100019 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Química |
publisher.none.fl_str_mv |
Sociedade Brasileira de Química |
dc.source.none.fl_str_mv |
Journal of the Brazilian Chemical Society v.22 n.1 2011 reponame:Journal of the Brazilian Chemical Society (Online) instname:Sociedade Brasileira de Química (SBQ) instacron:SBQ |
instname_str |
Sociedade Brasileira de Química (SBQ) |
instacron_str |
SBQ |
institution |
SBQ |
reponame_str |
Journal of the Brazilian Chemical Society (Online) |
collection |
Journal of the Brazilian Chemical Society (Online) |
repository.name.fl_str_mv |
Journal of the Brazilian Chemical Society (Online) - Sociedade Brasileira de Química (SBQ) |
repository.mail.fl_str_mv |
||office@jbcs.sbq.org.br |
_version_ |
1750318171871510528 |