Cargando…
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...
Autores principales: | , , , , , , |
---|---|
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 |
_version_ | 1784902305529724928 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9992408 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT sudkijuliamarconato fungalidentificationinpeanutsseedsthroughmultispectralimagestechnologicaladvancestoenhancesanitaryquality AT fonsecadeoliveiragustavoroberto fungalidentificationinpeanutsseedsthroughmultispectralimagestechnologicaladvancestoenhancesanitaryquality AT demedeirosandredantas fungalidentificationinpeanutsseedsthroughmultispectralimagestechnologicaladvancestoenhancesanitaryquality AT mastrangelothiago fungalidentificationinpeanutsseedsthroughmultispectralimagestechnologicaladvancestoenhancesanitaryquality AT arthurvalter fungalidentificationinpeanutsseedsthroughmultispectralimagestechnologicaladvancestoenhancesanitaryquality AT amaraldasilvaedvaldoaparecido fungalidentificationinpeanutsseedsthroughmultispectralimagestechnologicaladvancestoenhancesanitaryquality AT mastrangeloclissiabarboza fungalidentificationinpeanutsseedsthroughmultispectralimagestechnologicaladvancestoenhancesanitaryquality |