Optimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networks

Detalhes bibliográficos
Autor(a) principal: Bruno Soares Laurindo
Data de Publicação: 2017
Outros Autores: Renata Dias Freitas Laurindo, Alcinei Mistico Azevedo, Fábio Teixiera Delazari, José Cola Zanuncio, Derly José Henriques da Silva
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFMG
Texto Completo: http://dx.doi.org/10.1016/j.scienta.2017.02.005
http://hdl.handle.net/1843/40604
https://orcid.org/0000-0001-5196-0851
Resumo: CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
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spelling 2022-03-30T11:56:18Z2022-03-30T11:56:18Z2017218171176http://dx.doi.org/10.1016/j.scienta.2017.02.00503044238http://hdl.handle.net/1843/40604https://orcid.org/0000-0001-5196-0851CNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorThe efficacy of artificial neural networks (ANN) to solve complex problems can optimize evaluation processes for early blight disease on tomato plants, reducing required time and resources. The objective of the study was to verify the efficiency of ANN to predict the area under the disease progress curve (AUDPC) to reduce the number of assessments and establish the best time to evaluate early blight disease in tomato accessions. The severity of this disease was evaluated in one hundred and thirty-five tomato accessions from the Germplasm Vegetable Bank of the Federal University of Viçosa (BGH-UFV) in three experiments. The area under the disease progress curve (AUDPC) was calculated with data from six evaluations of the disease’s severity. Several ANN MLP types (Multi-Layer-Perceptron) were trained, taking into account AUDPC values for ​ desired output. Different numbers and assessment combinations for early blight disease severity were used as input. ANN’s were efficient at predicting AUDPC and reduced the number of evaluations from six to two. The twelfth and eighteenth days after pathogen inoculation are the best to evaluate the severity of early blight disease. Genotype by environment affects the efficiency in predicting the AUDPC. ANNs were efficient at predicting the area under the early blight disease progress curve (AUDPC) with fewer evaluations, and as such optimized assessment of this disease in tomato accessions.engUniversidade Federal de Minas GeraisUFMGBrasilICA - INSTITUTO DE CIÊNCIAS AGRÁRIASScientia HorticulturaeTomateRedes neurais (Computação)Inteligencia artificialTomate - Doenças e pragasGenetica vegetalOptimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networksinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://www.sciencedirect.com/science/article/pii/S0304423817300900?msclkid=af1b6270b01d11ec9d191774f906c1b0Bruno Soares LaurindoRenata Dias Freitas LaurindoAlcinei Mistico AzevedoFábio Teixiera DelazariJosé Cola ZanuncioDerly José Henriques da Silvainfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGORIGINALOptimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networks.pdfOptimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networks.pdfapplication/pdf520731https://repositorio.ufmg.br/bitstream/1843/40604/2/Optimization%20of%20the%20number%20of%20evaluations%20for%20early%20blight%20disease%20in%20tomato%20accessions%20using%20artificial%20neural%20networks.pdf58d4032289cfc57106a6e586120ddfffMD52LICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/40604/1/License.txtfa505098d172de0bc8864fc1287ffe22MD511843/406042022-03-30 08:56:18.712oai:repositorio.ufmg.br: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Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2022-03-30T11:56:18Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
dc.title.pt_BR.fl_str_mv Optimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networks
title Optimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networks
spellingShingle Optimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networks
Bruno Soares Laurindo
Tomate
Redes neurais (Computação)
Inteligencia artificial
Tomate - Doenças e pragas
Genetica vegetal
title_short Optimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networks
title_full Optimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networks
title_fullStr Optimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networks
title_full_unstemmed Optimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networks
title_sort Optimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networks
author Bruno Soares Laurindo
author_facet Bruno Soares Laurindo
Renata Dias Freitas Laurindo
Alcinei Mistico Azevedo
Fábio Teixiera Delazari
José Cola Zanuncio
Derly José Henriques da Silva
author_role author
author2 Renata Dias Freitas Laurindo
Alcinei Mistico Azevedo
Fábio Teixiera Delazari
José Cola Zanuncio
Derly José Henriques da Silva
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Bruno Soares Laurindo
Renata Dias Freitas Laurindo
Alcinei Mistico Azevedo
Fábio Teixiera Delazari
José Cola Zanuncio
Derly José Henriques da Silva
dc.subject.other.pt_BR.fl_str_mv Tomate
Redes neurais (Computação)
Inteligencia artificial
Tomate - Doenças e pragas
Genetica vegetal
topic Tomate
Redes neurais (Computação)
Inteligencia artificial
Tomate - Doenças e pragas
Genetica vegetal
description CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
publishDate 2017
dc.date.issued.fl_str_mv 2017
dc.date.accessioned.fl_str_mv 2022-03-30T11:56:18Z
dc.date.available.fl_str_mv 2022-03-30T11:56:18Z
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/1843/40604
dc.identifier.doi.pt_BR.fl_str_mv http://dx.doi.org/10.1016/j.scienta.2017.02.005
dc.identifier.issn.pt_BR.fl_str_mv 03044238
dc.identifier.orcid.pt_BR.fl_str_mv https://orcid.org/0000-0001-5196-0851
url http://dx.doi.org/10.1016/j.scienta.2017.02.005
http://hdl.handle.net/1843/40604
https://orcid.org/0000-0001-5196-0851
identifier_str_mv 03044238
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartof.pt_BR.fl_str_mv Scientia Horticulturae
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/1843/40604/2/Optimization%20of%20the%20number%20of%20evaluations%20for%20early%20blight%20disease%20in%20tomato%20accessions%20using%20artificial%20neural%20networks.pdf
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