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

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 t...

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Autores principales: Sudki, Julia Marconato, Fonseca de Oliveira, Gustavo Roberto, de Medeiros, André Dantas, Mastrangelo, Thiago, Arthur, Valter, Amaral da Silva, Edvaldo Aparecido, Mastrangelo, Clíssia Barboza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992408/
https://www.ncbi.nlm.nih.gov/pubmed/36909395
http://dx.doi.org/10.3389/fpls.2023.1112916
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author Sudki, Julia Marconato
Fonseca de Oliveira, Gustavo Roberto
de Medeiros, André Dantas
Mastrangelo, Thiago
Arthur, Valter
Amaral da Silva, Edvaldo Aparecido
Mastrangelo, Clíssia Barboza
author_facet Sudki, Julia Marconato
Fonseca de Oliveira, Gustavo Roberto
de Medeiros, André Dantas
Mastrangelo, Thiago
Arthur, Valter
Amaral da Silva, Edvaldo Aparecido
Mastrangelo, Clíssia Barboza
author_sort Sudki, Julia Marconato
collection PubMed
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.
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spelling pubmed-99924082023-03-09 Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality Sudki, Julia Marconato Fonseca de Oliveira, Gustavo Roberto de Medeiros, André Dantas Mastrangelo, Thiago Arthur, Valter Amaral da Silva, Edvaldo Aparecido Mastrangelo, Clíssia Barboza Front Plant Sci Plant Science 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. Frontiers Media S.A. 2023-02-22 /pmc/articles/PMC9992408/ /pubmed/36909395 http://dx.doi.org/10.3389/fpls.2023.1112916 Text en Copyright © 2023 Sudki, Fonseca de Oliveira, de Medeiros, Mastrangelo, Arthur, Amaral da Silva and Mastrangelo https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Sudki, Julia Marconato
Fonseca de Oliveira, Gustavo Roberto
de Medeiros, André Dantas
Mastrangelo, Thiago
Arthur, Valter
Amaral da Silva, Edvaldo Aparecido
Mastrangelo, Clíssia Barboza
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
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_short 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
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992408/
https://www.ncbi.nlm.nih.gov/pubmed/36909395
http://dx.doi.org/10.3389/fpls.2023.1112916
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