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

Detalhes bibliográficos
Autor(a) principal: Sudki, Julia Marconato
Data de Publicação: 2023
Outros Autores: Fonseca de Oliveira, Gustavo Roberto [UNESP], de Medeiros, André Dantas, Mastrangelo, Thiago, Arthur, Valter, Amaral da Silva, Edvaldo Aparecido [UNESP], Mastrangelo, Clíssia Barboza
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.3389/fpls.2023.1112916
http://hdl.handle.net/11449/248489
Resumo: The 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.
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spelling Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary qualityArachis hypogaeaLAspergillussppmachine learningseed healthsupport vector machineThe 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.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Laboratory of Radiobiology and Environment Center for Nuclear Energy in Agriculture University of São Paulo (CENA/USP), SPDepartment of Crop Science College of Agricultural Sciences Faculdade de Ciências Agronômicas (FCA) São Paulo State University (UNESP)Department of Agronomy Federal University of Viçosa (UFV)Department of Crop Science College of Agricultural Sciences Faculdade de Ciências Agronômicas (FCA) São Paulo State University (UNESP)Universidade de São Paulo (USP)Universidade Estadual Paulista (UNESP)Universidade Federal de Viçosa (UFV)Sudki, Julia MarconatoFonseca de Oliveira, Gustavo Roberto [UNESP]de Medeiros, André DantasMastrangelo, ThiagoArthur, ValterAmaral da Silva, Edvaldo Aparecido [UNESP]Mastrangelo, Clíssia Barboza2023-07-29T13:45:28Z2023-07-29T13:45:28Z2023-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3389/fpls.2023.1112916Frontiers in Plant Science, v. 14.1664-462Xhttp://hdl.handle.net/11449/24848910.3389/fpls.2023.11129162-s2.0-85149730067Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengFrontiers in Plant Scienceinfo:eu-repo/semantics/openAccess2024-04-30T15:59:39Zoai:repositorio.unesp.br:11449/248489Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:44:47.391762Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality
title Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality
spellingShingle Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality
Sudki, Julia Marconato
Arachis hypogaeaL
Aspergillusspp
machine learning
seed health
support vector machine
title_short Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality
title_full Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality
title_fullStr Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality
title_full_unstemmed Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality
title_sort Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality
author Sudki, Julia Marconato
author_facet Sudki, Julia Marconato
Fonseca de Oliveira, Gustavo Roberto [UNESP]
de Medeiros, André Dantas
Mastrangelo, Thiago
Arthur, Valter
Amaral da Silva, Edvaldo Aparecido [UNESP]
Mastrangelo, Clíssia Barboza
author_role author
author2 Fonseca de Oliveira, Gustavo Roberto [UNESP]
de Medeiros, André Dantas
Mastrangelo, Thiago
Arthur, Valter
Amaral da Silva, Edvaldo Aparecido [UNESP]
Mastrangelo, Clíssia Barboza
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade de São Paulo (USP)
Universidade Estadual Paulista (UNESP)
Universidade Federal de Viçosa (UFV)
dc.contributor.author.fl_str_mv Sudki, Julia Marconato
Fonseca de Oliveira, Gustavo Roberto [UNESP]
de Medeiros, André Dantas
Mastrangelo, Thiago
Arthur, Valter
Amaral da Silva, Edvaldo Aparecido [UNESP]
Mastrangelo, Clíssia Barboza
dc.subject.por.fl_str_mv Arachis hypogaeaL
Aspergillusspp
machine learning
seed health
support vector machine
topic Arachis hypogaeaL
Aspergillusspp
machine learning
seed health
support vector machine
description The 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.
publishDate 2023
dc.date.none.fl_str_mv 2023-07-29T13:45:28Z
2023-07-29T13:45:28Z
2023-01-01
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://dx.doi.org/10.3389/fpls.2023.1112916
Frontiers in Plant Science, v. 14.
1664-462X
http://hdl.handle.net/11449/248489
10.3389/fpls.2023.1112916
2-s2.0-85149730067
url http://dx.doi.org/10.3389/fpls.2023.1112916
http://hdl.handle.net/11449/248489
identifier_str_mv Frontiers in Plant Science, v. 14.
1664-462X
10.3389/fpls.2023.1112916
2-s2.0-85149730067
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Frontiers in Plant Science
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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