FUZZY MODELING OF THE EFFECTS OF DIFFERENT IRRIGATION DEPTH IN RADISH CROP. PART II: BIOMETRIC VARIABLES ANALYSIS
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v41n3p319-329/2021 http://hdl.handle.net/11449/211232 |
Resumo: | In 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. |
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FUZZY MODELING OF THE EFFECTS OF DIFFERENT IRRIGATION DEPTH IN RADISH CROP. PART II: BIOMETRIC VARIABLES ANALYSISbiometric variablesfuzzy logicirrigation depthpolynomial regressionneural networkIn 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.São Paulo State University, School of AgricultureSão Paulo State University, School of Sciences and EngineeringSão Paulo State University, School of AgricultureSão Paulo State University, School of Sciences and EngineeringAssociação Brasileira de Engenharia AgrícolaUniversidade Estadual Paulista (Unesp)Boso, Ana C. M. R. [UNESP]Cremasco, Camila P. [UNESP]Putti, Fernando F. [UNESP]Gabriel, Luís R. A. [UNESP]2021-07-14T10:21:14Z2021-07-14T10:21:14Z2021-06-25info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article319-329application/pdfhttp://dx.doi.org/10.1590/1809-4430-Eng.Agric.v41n3p319-329/2021Engenharia Agrícola. Associação Brasileira de Engenharia Agrícola, v. 41, n. 3, p. 319-329, 2021.0100-69161809-4430http://hdl.handle.net/11449/21123210.1590/1809-4430-Eng.Agric.v41n3p319-329/2021S0100-69162021000300319S0100-69162021000300319.pdfSciELOreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEngenharia Agrícolainfo:eu-repo/semantics/openAccess2023-11-01T06:08:45Zoai:repositorio.unesp.br:11449/211232Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-11-01T06:08:45Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
FUZZY MODELING OF THE EFFECTS OF DIFFERENT IRRIGATION DEPTH IN RADISH CROP. PART II: BIOMETRIC VARIABLES ANALYSIS |
title |
FUZZY MODELING OF THE EFFECTS OF DIFFERENT IRRIGATION DEPTH IN RADISH CROP. PART II: BIOMETRIC VARIABLES ANALYSIS |
spellingShingle |
FUZZY MODELING OF THE EFFECTS OF DIFFERENT IRRIGATION DEPTH IN RADISH CROP. PART II: BIOMETRIC VARIABLES ANALYSIS Boso, Ana C. M. R. [UNESP] biometric variables fuzzy logic irrigation depth polynomial regression neural network |
title_short |
FUZZY MODELING OF THE EFFECTS OF DIFFERENT IRRIGATION DEPTH IN RADISH CROP. PART II: BIOMETRIC VARIABLES ANALYSIS |
title_full |
FUZZY MODELING OF THE EFFECTS OF DIFFERENT IRRIGATION DEPTH IN RADISH CROP. PART II: BIOMETRIC VARIABLES ANALYSIS |
title_fullStr |
FUZZY MODELING OF THE EFFECTS OF DIFFERENT IRRIGATION DEPTH IN RADISH CROP. PART II: BIOMETRIC VARIABLES ANALYSIS |
title_full_unstemmed |
FUZZY MODELING OF THE EFFECTS OF DIFFERENT IRRIGATION DEPTH IN RADISH CROP. PART II: BIOMETRIC VARIABLES ANALYSIS |
title_sort |
FUZZY MODELING OF THE EFFECTS OF DIFFERENT IRRIGATION DEPTH IN RADISH CROP. PART II: BIOMETRIC VARIABLES ANALYSIS |
author |
Boso, Ana C. M. R. [UNESP] |
author_facet |
Boso, Ana C. M. R. [UNESP] Cremasco, Camila P. [UNESP] Putti, Fernando F. [UNESP] Gabriel, Luís R. A. [UNESP] |
author_role |
author |
author2 |
Cremasco, Camila P. [UNESP] Putti, Fernando F. [UNESP] Gabriel, Luís R. A. [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Boso, Ana C. M. R. [UNESP] Cremasco, Camila P. [UNESP] Putti, Fernando F. [UNESP] Gabriel, Luís R. A. [UNESP] |
dc.subject.por.fl_str_mv |
biometric variables fuzzy logic irrigation depth polynomial regression neural network |
topic |
biometric variables fuzzy logic irrigation depth polynomial regression neural network |
description |
In 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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-07-14T10:21:14Z 2021-07-14T10:21:14Z 2021-06-25 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v41n3p319-329/2021 Engenharia Agrícola. Associação Brasileira de Engenharia Agrícola, v. 41, n. 3, p. 319-329, 2021. 0100-6916 1809-4430 http://hdl.handle.net/11449/211232 10.1590/1809-4430-Eng.Agric.v41n3p319-329/2021 S0100-69162021000300319 S0100-69162021000300319.pdf |
url |
http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v41n3p319-329/2021 http://hdl.handle.net/11449/211232 |
identifier_str_mv |
Engenharia Agrícola. Associação Brasileira de Engenharia Agrícola, v. 41, n. 3, p. 319-329, 2021. 0100-6916 1809-4430 10.1590/1809-4430-Eng.Agric.v41n3p319-329/2021 S0100-69162021000300319 S0100-69162021000300319.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Engenharia Agrícola |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
319-329 application/pdf |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
dc.source.none.fl_str_mv |
SciELO 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 |
|
_version_ |
1799964776749596672 |