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

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dc.contributor.authorBianconi, A.
dc.contributor.authorWatts, M. J.
dc.contributor.authorHuang, Y.
dc.contributor.authorSerapiao, A. B. S. [UNESP]
dc.contributor.authorGovone, J. S. [UNESP]
dc.contributor.authorMi, X.
dc.contributor.authorHabermann, G. [UNESP]
dc.contributor.authorFerrarini, A.
dc.contributor.authorIEEE
dc.contributor.institutionInt Acad Ecol & Environm Sci
dc.contributor.institutionAuckland Inst Studies
dc.contributor.institutionARS
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionChinese Acad Sci
dc.contributor.institutionUniv Parma
dc.date.accessioned2019-10-04T12:30:07Z
dc.date.available2019-10-04T12:30:07Z
dc.date.issued2014-01-01
dc.description.abstractThe 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.en
dc.description.affiliationInt Acad Ecol & Environm Sci, Hong Kong, Hong Kong, Peoples R China
dc.description.affiliationAuckland Inst Studies, Informat Technol Programme, Auckland, New Zealand
dc.description.affiliationARS, USDA, Crop Prod Syst Res Unit, Mississippi State, MS USA
dc.description.affiliationIGCE DEMAC Unesp, Rio Claro, SP, Brazil
dc.description.affiliationChinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing, Peoples R China
dc.description.affiliationUNESP, Inst Biociencias, Rio Claro, SP, Brazil
dc.description.affiliationUniv Parma, I-43100 Parma, Italy
dc.description.affiliationUnespIGCE DEMAC Unesp, Rio Claro, SP, Brazil
dc.description.affiliationUnespUNESP, Inst Biociencias, Rio Claro, SP, Brazil
dc.format.extent658-662
dc.identifier.citationProceedings Of The 2014 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, p. 658-662, 2014.
dc.identifier.issn2161-4393
dc.identifier.urihttp://hdl.handle.net/11449/184787
dc.identifier.wosWOS:000371465700097
dc.language.isoeng
dc.publisherIeee
dc.relation.ispartofProceedings Of The 2014 International Joint Conference On Neural Networks (ijcnn)
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.titleApplying Computational Intelligence Methods to Modeling and Predicting Common Bean Germination Ratesen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIeee

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