FUZZY MODELING OF THE EFFECTS OF DIFFERENT IRRIGATION DEPTH IN RADISH CROP. PART II: BIOMETRIC VARIABLES ANALYSIS

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dc.contributor.authorBoso, Ana C. M. R. [UNESP]
dc.contributor.authorCremasco, Camila P. [UNESP]
dc.contributor.authorPutti, Fernando F. [UNESP]
dc.contributor.authorGabriel, Luís R. A. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2021-07-14T10:21:14Z
dc.date.available2021-07-14T10:21:14Z
dc.date.issued2021-06-25
dc.description.abstractIn order to estimate the response of biometric variables in different irrigation depths in radish crop, as well as their relations in the development of the crop, a fuzzy mathematical analysis was carried out from irrigation with depths of different percentages of the crop evapotranspiration (ETc), using Gaussian pertinence functions for the input variable and triangular for the biometric output variables. Validations were performed using neural network models, smoothing splines and polynomial regression. The relation among the biometric variables was measured applying the Pearson correlation coefficient. The results showed that the fuzzy modeling presented superiority in the crop development estimate over the quadratic polynomial regression model, neural network and smoothing splines, because it achieved an average reduction of errors among the biometric variables, of 7.8% 94.6% and 9.2% for the RMSE in the respective models, as well as a better adjustment of the data with average R2 of the variables. The modeling with neural network showed inadequate agronomic behavior in data representation. Regarding biometric variables, the length and diameter of the tuberous root are inversely correlated, and the fresh phytomass of the tuberous root is correlated only with the fresh phytomass of the root.en
dc.description.affiliationSão Paulo State University, School of Agriculture
dc.description.affiliationSão Paulo State University, School of Sciences and Engineering
dc.description.affiliationUnespSão Paulo State University, School of Agriculture
dc.description.affiliationUnespSão Paulo State University, School of Sciences and Engineering
dc.format.extent319-329
dc.identifierhttp://dx.doi.org/10.1590/1809-4430-Eng.Agric.v41n3p319-329/2021
dc.identifier.citationEngenharia Agrícola. Associação Brasileira de Engenharia Agrícola, v. 41, n. 3, p. 319-329, 2021.
dc.identifier.doi10.1590/1809-4430-Eng.Agric.v41n3p319-329/2021
dc.identifier.fileS0100-69162021000300319.pdf
dc.identifier.issn0100-6916
dc.identifier.issn1809-4430
dc.identifier.scieloS0100-69162021000300319
dc.identifier.urihttp://hdl.handle.net/11449/211232
dc.language.isoeng
dc.publisherAssociação Brasileira de Engenharia Agrícola
dc.relation.ispartofEngenharia Agrícola
dc.rights.accessRightsAcesso aberto
dc.sourceSciELO
dc.subjectbiometric variablesen
dc.subjectfuzzy logicen
dc.subjectirrigation depthen
dc.subjectpolynomial regressionen
dc.subjectneural networken
dc.titleFUZZY MODELING OF THE EFFECTS OF DIFFERENT IRRIGATION DEPTH IN RADISH CROP. PART II: BIOMETRIC VARIABLES ANALYSISen
dc.typeArtigo
unesp.author.orcid0000-0002-7269-2806[4]

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