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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/40604
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dc.creatorBruno Soares Laurindopt_BR
dc.creatorRenata Dias Freitas Laurindopt_BR
dc.creatorAlcinei Mistico Azevedopt_BR
dc.creatorFábio Teixiera Delazaript_BR
dc.creatorJosé Cola Zanunciopt_BR
dc.creatorDerly José Henriques da Silvapt_BR
dc.date.accessioned2022-03-30T11:56:18Z-
dc.date.available2022-03-30T11:56:18Z-
dc.date.issued2017-
dc.citation.volume218pt_BR
dc.citation.spage171pt_BR
dc.citation.epage176pt_BR
dc.identifier.doihttp://dx.doi.org/10.1016/j.scienta.2017.02.005pt_BR
dc.identifier.issn03044238pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/40604-
dc.description.resumoThe 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.pt_BR
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológicopt_BR
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIASpt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofScientia Horticulturaept_BR
dc.rightsAcesso Abertopt_BR
dc.subject.otherTomatept_BR
dc.subject.otherRedes neurais (Computação)pt_BR
dc.subject.otherInteligencia artificialpt_BR
dc.subject.otherTomate - Doenças e pragaspt_BR
dc.subject.otherGenetica vegetalpt_BR
dc.titleOptimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networkspt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.sciencedirect.com/science/article/pii/S0304423817300900?msclkid=af1b6270b01d11ec9d191774f906c1b0pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-5196-0851pt_BR
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