Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , |
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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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 |
|
_version_ |
1808129457514348544 |