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

Página do item simplificado
dc.creatorBruno Soares Laurindo
dc.creatorRenata Dias Freitas Laurindo
dc.creatorAlcinei Mistico Azevedo
dc.creatorFábio Teixiera Delazari
dc.creatorJosé Cola Zanuncio
dc.creatorDerly José Henriques da Silva
dc.date.accessioned2022-03-30T11:56:18Z
dc.date.accessioned2025-09-08T23:17:09Z
dc.date.available2022-03-30T11:56:18Z
dc.date.issued2017
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.identifier.doihttp://dx.doi.org/10.1016/j.scienta.2017.02.005
dc.identifier.issn03044238
dc.identifier.urihttps://hdl.handle.net/1843/40604
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofScientia Horticulturae
dc.rightsAcesso Aberto
dc.subjectTomate
dc.subjectRedes neurais (Computação)
dc.subjectInteligencia artificial
dc.subjectTomate - Doenças e pragas
dc.subjectGenetica vegetal
dc.titleOptimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networks
dc.typeArtigo de periódico
local.citation.epage176
local.citation.spage171
local.citation.volume218
local.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.
local.identifier.orcidhttps://orcid.org/0000-0001-5196-0851
local.publisher.countryBrasil
local.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S0304423817300900?msclkid=af1b6270b01d11ec9d191774f906c1b0

Arquivos

Pacote original

Agora exibindo 1 - 1 de 1
Imagem de Miniatura
Nome:
Optimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networks.pdf
Tamanho:
508.53 KB
Formato:
Adobe Portable Document Format
Baixar

Licença do pacote

Agora exibindo 1 - 1 de 1
Imagem de Miniatura
Nome:
License.txt
Tamanho:
1.99 KB
Formato:
Plain Text
Descrição:
Baixar

Coleções

Artigo de Periódico

Logo UFMG