Artificial neural network modelling in the prediction of bananas’ harvest

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dc.contributor.authorde Souza, Angela Vacaro [UNESP]
dc.contributor.authorBonini Neto, Alfredo [UNESP]
dc.contributor.authorCabrera Piazentin, Jhonatan [UNESP]
dc.contributor.authorDainese Junior, Bruno José
dc.contributor.authorPerin Gomes, Estevão [UNESP]
dc.contributor.authordos Santos Batista Bonini, Carolina [UNESP]
dc.contributor.authorFerrari Putti, Fernando [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionEduvale College of Avaré
dc.date.accessioned2020-12-12T02:27:47Z
dc.date.available2020-12-12T02:27:47Z
dc.date.issued2019-11-17
dc.description.abstractBanana tree (Musa spp.) is responsible for providing one of the most consumed and appreciated fruits in all regions of the world, and is cultivated mainly in tropical countries. In this connection, several management systems have been developed to simulate growth, yield, as well as the production of several crops according to climatic data. This study seeks to investigate the relationship of climatic variables in the banana bunch gestation period in order to predict the time of production. For that purpose, it was used an artificial neural network to estimate the bananas’ harvest period in subtropical regions. The experiment was conducted for 7 cycles/years using ‘Nanicão’ cultivar. Climatological data were measured by automatic stations. According to the results’ analysis, it can be verified that the estimation of the harvest through artificial neural networks presented 0.3% error and coefficient of determination of R2 of 89%. From the developed model it was possible to establish the banana harvest forecast. It can be verified that the RNAs present a high percentage of correctness in the collection of the harvest, this is confirmed by the low square error. In this way, the model becomes a management tool for banana producers to help forecast demand.en
dc.description.affiliationSão Paulo State University (UNESP) School of Science and Engineering
dc.description.affiliationSão Paulo State University (UNESP) Department of Rural Engineering
dc.description.affiliationEduvale College of Avaré
dc.description.affiliationSão Paulo State University (UNESP) Department of Plant Production - Horticulture
dc.description.affiliationSão Paulo State University (UNESP) College of Agricultural and Technological Sciences
dc.description.affiliationUnespSão Paulo State University (UNESP) School of Science and Engineering
dc.description.affiliationUnespSão Paulo State University (UNESP) Department of Rural Engineering
dc.description.affiliationUnespSão Paulo State University (UNESP) Department of Plant Production - Horticulture
dc.description.affiliationUnespSão Paulo State University (UNESP) College of Agricultural and Technological Sciences
dc.identifierhttp://dx.doi.org/10.1016/j.scienta.2019.108724
dc.identifier.citationScientia Horticulturae, v. 257.
dc.identifier.doi10.1016/j.scienta.2019.108724
dc.identifier.issn0304-4238
dc.identifier.scopus2-s2.0-85073705721
dc.identifier.urihttp://hdl.handle.net/11449/201248
dc.language.isoeng
dc.relation.ispartofScientia Horticulturae
dc.sourceScopus
dc.subjectMathematical modeling
dc.subjectMusa acuminate ‘Dwarf Cavendish’
dc.subjectProductivity
dc.titleArtificial neural network modelling in the prediction of bananas’ harvesten
dc.typeArtigo
unesp.author.lattes9580260484174480[6]
unesp.author.orcid0000-0002-4647-2391[1]
unesp.author.orcid0000-0002-6482-3263[6]
unesp.author.orcid0000-0002-0555-9271[7]

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