Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality.
Arachis hypogaea L.
Aspergillus spp.
machine learning
seed health
support vector machine
Journal
Frontiers in plant science
ISSN: 1664-462X
Titre abrégé: Front Plant Sci
Pays: Switzerland
ID NLM: 101568200
Informations de publication
Date de publication:
2023
2023
Historique:
received:
30
11
2022
accepted:
07
02
2023
entrez:
13
3
2023
pubmed:
14
3
2023
medline:
14
3
2023
Statut:
epublish
Résumé
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 (
Identifiants
pubmed: 36909395
doi: 10.3389/fpls.2023.1112916
pmc: PMC9992408
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1112916Informations de copyright
Copyright © 2023 Sudki, Fonseca de Oliveira, de Medeiros, Mastrangelo, Arthur, Amaral da Silva and Mastrangelo.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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