Applying Computational Intelligence Methods to Modeling and Predicting Common Bean Germination Rates

Detalhes bibliográficos
Autor(a) principal: Bianconi, A.
Data de Publicação: 2014
Outros Autores: Watts, M. J., Huang, Y., Serapiao, A. B. S. [UNESP], Govone, J. S. [UNESP], Mi, X., Habermann, G. [UNESP], Ferrarini, A., IEEE
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/184787
Resumo: The relationship between seed germination rate and environmental temperature is complex. This study assessed the effectiveness of multi-layer perceptron (MLP) and Particle Swarm Optimization (PSO) techniques in modeling and predicting the germination rate of two common bean cultivars as a function of distinct temperatures. MLP was utilized to model the germination rate of the cultivars and PSO was employed to determine the optimum temperatures at which the beans germinate most rapidly. The outcomes derived from implementing the MLP were compared with those obtained by means of a traditional statistical method. The MLP provided more accurate results than the conventional statistical regression in predicting germination rate values regarding the two common bean cultivars. The optimum germination rate values derived from implementing the PSO model were more accurate than those obtained by using the conventional quadratic regression.
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spelling Applying Computational Intelligence Methods to Modeling and Predicting Common Bean Germination RatesThe relationship between seed germination rate and environmental temperature is complex. This study assessed the effectiveness of multi-layer perceptron (MLP) and Particle Swarm Optimization (PSO) techniques in modeling and predicting the germination rate of two common bean cultivars as a function of distinct temperatures. MLP was utilized to model the germination rate of the cultivars and PSO was employed to determine the optimum temperatures at which the beans germinate most rapidly. The outcomes derived from implementing the MLP were compared with those obtained by means of a traditional statistical method. The MLP provided more accurate results than the conventional statistical regression in predicting germination rate values regarding the two common bean cultivars. The optimum germination rate values derived from implementing the PSO model were more accurate than those obtained by using the conventional quadratic regression.Int Acad Ecol & Environm Sci, Hong Kong, Hong Kong, Peoples R ChinaAuckland Inst Studies, Informat Technol Programme, Auckland, New ZealandARS, USDA, Crop Prod Syst Res Unit, Mississippi State, MS USAIGCE DEMAC Unesp, Rio Claro, SP, BrazilChinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing, Peoples R ChinaUNESP, Inst Biociencias, Rio Claro, SP, BrazilUniv Parma, I-43100 Parma, ItalyIGCE DEMAC Unesp, Rio Claro, SP, BrazilUNESP, Inst Biociencias, Rio Claro, SP, BrazilIeeeInt Acad Ecol & Environm SciAuckland Inst StudiesARSUniversidade Estadual Paulista (Unesp)Chinese Acad SciUniv ParmaBianconi, A.Watts, M. J.Huang, Y.Serapiao, A. B. S. [UNESP]Govone, J. S. [UNESP]Mi, X.Habermann, G. [UNESP]Ferrarini, A.IEEE2019-10-04T12:30:07Z2019-10-04T12:30:07Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject658-662Proceedings Of The 2014 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, p. 658-662, 2014.2161-4393http://hdl.handle.net/11449/184787WOS:000371465700097Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings Of The 2014 International Joint Conference On Neural Networks (ijcnn)info:eu-repo/semantics/openAccess2021-10-23T02:05:39Zoai:repositorio.unesp.br:11449/184787Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:46:25.468306Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Applying Computational Intelligence Methods to Modeling and Predicting Common Bean Germination Rates
title Applying Computational Intelligence Methods to Modeling and Predicting Common Bean Germination Rates
spellingShingle Applying Computational Intelligence Methods to Modeling and Predicting Common Bean Germination Rates
Bianconi, A.
title_short Applying Computational Intelligence Methods to Modeling and Predicting Common Bean Germination Rates
title_full Applying Computational Intelligence Methods to Modeling and Predicting Common Bean Germination Rates
title_fullStr Applying Computational Intelligence Methods to Modeling and Predicting Common Bean Germination Rates
title_full_unstemmed Applying Computational Intelligence Methods to Modeling and Predicting Common Bean Germination Rates
title_sort Applying Computational Intelligence Methods to Modeling and Predicting Common Bean Germination Rates
author Bianconi, A.
author_facet Bianconi, A.
Watts, M. J.
Huang, Y.
Serapiao, A. B. S. [UNESP]
Govone, J. S. [UNESP]
Mi, X.
Habermann, G. [UNESP]
Ferrarini, A.
IEEE
author_role author
author2 Watts, M. J.
Huang, Y.
Serapiao, A. B. S. [UNESP]
Govone, J. S. [UNESP]
Mi, X.
Habermann, G. [UNESP]
Ferrarini, A.
IEEE
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Int Acad Ecol & Environm Sci
Auckland Inst Studies
ARS
Universidade Estadual Paulista (Unesp)
Chinese Acad Sci
Univ Parma
dc.contributor.author.fl_str_mv Bianconi, A.
Watts, M. J.
Huang, Y.
Serapiao, A. B. S. [UNESP]
Govone, J. S. [UNESP]
Mi, X.
Habermann, G. [UNESP]
Ferrarini, A.
IEEE
description The relationship between seed germination rate and environmental temperature is complex. This study assessed the effectiveness of multi-layer perceptron (MLP) and Particle Swarm Optimization (PSO) techniques in modeling and predicting the germination rate of two common bean cultivars as a function of distinct temperatures. MLP was utilized to model the germination rate of the cultivars and PSO was employed to determine the optimum temperatures at which the beans germinate most rapidly. The outcomes derived from implementing the MLP were compared with those obtained by means of a traditional statistical method. The MLP provided more accurate results than the conventional statistical regression in predicting germination rate values regarding the two common bean cultivars. The optimum germination rate values derived from implementing the PSO model were more accurate than those obtained by using the conventional quadratic regression.
publishDate 2014
dc.date.none.fl_str_mv 2014-01-01
2019-10-04T12:30:07Z
2019-10-04T12:30:07Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv Proceedings Of The 2014 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, p. 658-662, 2014.
2161-4393
http://hdl.handle.net/11449/184787
WOS:000371465700097
identifier_str_mv Proceedings Of The 2014 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, p. 658-662, 2014.
2161-4393
WOS:000371465700097
url http://hdl.handle.net/11449/184787
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings Of The 2014 International Joint Conference On Neural Networks (ijcnn)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 658-662
dc.publisher.none.fl_str_mv Ieee
publisher.none.fl_str_mv Ieee
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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