Comparison of methods for harvest prediction in 'gigante' cactus pear

Detalhes bibliográficos
Autor(a) principal: Bruno Vinícius Castro Guimarães
Data de Publicação: 2019
Outros Autores: Sérgio Luiz Rodrigues Donato, Ignacio Aspiazú, Alcinei Mistico Azevedo, Abner José de Carvalho
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFMG
Texto Completo: https://doi.org/10.5539/jas.v11n14p216
http://hdl.handle.net/1843/40465
https://orcid.org/0000-0001-5196-0851
Resumo: CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
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spelling 2022-03-25T12:28:25Z2022-03-25T12:28:25Z20191114216224https://doi.org/10.5539/jas.v11n14p21619169760http://hdl.handle.net/1843/40465https://orcid.org/0000-0001-5196-0851CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorOutra AgênciaBehavior analysis and plant expression are the answers the researcher needs to construct predictive models that minimize the effects of the uncertainties of field production. The objective of this study was to compare the simple and multiple linear regression methods and the artificial neural networks to allow the maximum security in the prediction of harvest in ‘Gigante’ cactus pear. The uniformity test was conducted at the Federal Institute of Bahia, Campus Guanambi, Bahia, Brazil, coordinates 14°13′30″ S, 42°46′53″ W and altitude of 525 m. At 930 days after planting, we evaluated 384 basic units, in which were measured the following variables: plant height (PH); cladode length (CL), width (CW) and thickness (CT); cladode number (CN); total cladode area (TCA); cladode area (CA) and cladode yield (Y). For the comparison between the artificial neural networks (ANN) and regression models (single and multiple-SLR and MLR), we considered the mean prediction error (MPE), the mean quadratic error (MQE), the mean square of deviation (MSD) and the coefficient of determination (R2).The values estimated by the ANN 7-5-1 showed the best proximity to the data obtained in field conditions, followed by ANN 6-2-1, MLR (TCA and CT), SLR (TCA) and SLR (CN). In this way, the ANN models with the topologies 7-2-1 and 6-2-1, MLR with the variables total cladode area and cladode thickness and SLR with the isolated descriptors total cladode area and cladode number, explain 85.1; 81.5; 76.3; 74.09 and 65.87%, respectively, of the yield variation. The ANNs were more efficient at predicting the yield of the ‘Gigante’ cactus pear when compared to the simple and multiple linear regression models.engUniversidade Federal de Minas GeraisUFMGBrasilICA - INSTITUTO DE CIÊNCIAS AGRÁRIASJournal of agricultural scienceCactoPalma forrageiraÉpoca de colheitaRedes neurais (Computação)Análise de variânciaComparison of methods for harvest prediction in 'gigante' cactus pearinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://www.ccsenet.org/journal/index.php/jas/article/view/0/40370?msclkid=02cff214ac3411eca17dec86d5f1f48fBruno Vinícius Castro GuimarãesSérgio Luiz Rodrigues DonatoIgnacio AspiazúAlcinei Mistico AzevedoAbner José de Carvalhoinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/40465/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINALComparison of Methods for Harvest Prediction in ‘Gigante’ Cactus Pear.pdfComparison of Methods for Harvest Prediction in ‘Gigante’ Cactus Pear.pdfapplication/pdf955902https://repositorio.ufmg.br/bitstream/1843/40465/2/Comparison%20of%20Methods%20for%20Harvest%20Prediction%20in%20%e2%80%98Gigante%e2%80%99%20Cactus%20Pear.pdf96c45820634cc72b6d306044b320651aMD521843/404652022-03-25 09:28:26.328oai:repositorio.ufmg.br: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Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2022-03-25T12:28:26Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
dc.title.pt_BR.fl_str_mv Comparison of methods for harvest prediction in 'gigante' cactus pear
title Comparison of methods for harvest prediction in 'gigante' cactus pear
spellingShingle Comparison of methods for harvest prediction in 'gigante' cactus pear
Bruno Vinícius Castro Guimarães
Cacto
Palma forrageira
Época de colheita
Redes neurais (Computação)
Análise de variância
title_short Comparison of methods for harvest prediction in 'gigante' cactus pear
title_full Comparison of methods for harvest prediction in 'gigante' cactus pear
title_fullStr Comparison of methods for harvest prediction in 'gigante' cactus pear
title_full_unstemmed Comparison of methods for harvest prediction in 'gigante' cactus pear
title_sort Comparison of methods for harvest prediction in 'gigante' cactus pear
author Bruno Vinícius Castro Guimarães
author_facet Bruno Vinícius Castro Guimarães
Sérgio Luiz Rodrigues Donato
Ignacio Aspiazú
Alcinei Mistico Azevedo
Abner José de Carvalho
author_role author
author2 Sérgio Luiz Rodrigues Donato
Ignacio Aspiazú
Alcinei Mistico Azevedo
Abner José de Carvalho
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Bruno Vinícius Castro Guimarães
Sérgio Luiz Rodrigues Donato
Ignacio Aspiazú
Alcinei Mistico Azevedo
Abner José de Carvalho
dc.subject.other.pt_BR.fl_str_mv Cacto
Palma forrageira
Época de colheita
Redes neurais (Computação)
Análise de variância
topic Cacto
Palma forrageira
Época de colheita
Redes neurais (Computação)
Análise de variância
description CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
publishDate 2019
dc.date.issued.fl_str_mv 2019
dc.date.accessioned.fl_str_mv 2022-03-25T12:28:25Z
dc.date.available.fl_str_mv 2022-03-25T12:28:25Z
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://hdl.handle.net/1843/40465
dc.identifier.doi.pt_BR.fl_str_mv https://doi.org/10.5539/jas.v11n14p216
dc.identifier.issn.pt_BR.fl_str_mv 19169760
dc.identifier.orcid.pt_BR.fl_str_mv https://orcid.org/0000-0001-5196-0851
url https://doi.org/10.5539/jas.v11n14p216
http://hdl.handle.net/1843/40465
https://orcid.org/0000-0001-5196-0851
identifier_str_mv 19169760
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartof.pt_BR.fl_str_mv Journal of agricultural science
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.publisher.initials.fl_str_mv UFMG
dc.publisher.country.fl_str_mv Brasil
dc.publisher.department.fl_str_mv ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFMG
instname:Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
instname_str Universidade Federal de Minas Gerais (UFMG)
instacron_str UFMG
institution UFMG
reponame_str Repositório Institucional da UFMG
collection Repositório Institucional da UFMG
bitstream.url.fl_str_mv https://repositorio.ufmg.br/bitstream/1843/40465/1/License.txt
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