Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Federal de Viçosa (UFV)-
Autor(es): dc.creatorSudki, Julia Marconato-
Autor(es): dc.creatorFonseca de Oliveira, Gustavo Roberto-
Autor(es): dc.creatorde Medeiros, André Dantas-
Autor(es): dc.creatorMastrangelo, Thiago-
Autor(es): dc.creatorArthur, Valter-
Autor(es): dc.creatorAmaral da Silva, Edvaldo Aparecido-
Autor(es): dc.creatorMastrangelo, Clíssia Barboza-
Data de aceite: dc.date.accessioned2025-08-21T22:20:24Z-
Data de disponibilização: dc.date.available2025-08-21T22:20:24Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3389/fpls.2023.1112916-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/248489-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/248489-
Descrição: dc.descriptionThe sanitary quality of seed is essential in agriculture. This is because pathogenic fungi compromise seed physiological quality and prevent the formation of plants in the field, which causes losses to farmers. Multispectral images technologies coupled with machine learning algorithms can optimize the identification of healthy peanut seeds, greatly improving the sanitary quality. The objective was to verify whether multispectral images technologies and artificial intelligence tools are effective for discriminating pathogenic fungi in tropical peanut seeds. For this purpose, dry peanut seeds infected by fungi (A. flavus, A. niger, Penicillium sp., and Rhizopus sp.) were used to acquire images at different wavelengths (365 to 970 nm). Multispectral markers of peanut seed health quality were found. The incubation period of 216 h was the one that most contributed to discriminating healthy seeds from those containing fungi through multispectral images. Texture (Percent Run), color (CIELab L*) and reflectance (490 nm) were highly effective in discriminating the sanitary quality of peanut seeds. Machine learning algorithms (LDA, MLP, RF, and SVM) demonstrated high accuracy in autonomous detection of seed health status (90 to 100%). Thus, multispectral images coupled with machine learning algorithms are effective for screening peanut seeds with superior sanitary quality.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionLaboratory of Radiobiology and Environment Center for Nuclear Energy in Agriculture University of São Paulo (CENA/USP), SP-
Descrição: dc.descriptionDepartment of Crop Science College of Agricultural Sciences Faculdade de Ciências Agronômicas (FCA) São Paulo State University (UNESP)-
Descrição: dc.descriptionDepartment of Agronomy Federal University of Viçosa (UFV)-
Descrição: dc.descriptionDepartment of Crop Science College of Agricultural Sciences Faculdade de Ciências Agronômicas (FCA) São Paulo State University (UNESP)-
Idioma: dc.languageen-
Relação: dc.relationFrontiers in Plant Science-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectArachis hypogaeaL-
Palavras-chave: dc.subjectAspergillusspp-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectseed health-
Palavras-chave: dc.subjectsupport vector machine-
Título: dc.titleFungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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