ScabyNet, a user-friendly application for detecting common scab in potato tubers using deep learning and morphological traits.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
13 Jan 2024
13 Jan 2024
Historique:
received:
30
05
2023
accepted:
30
12
2023
medline:
14
1
2024
pubmed:
14
1
2024
entrez:
13
1
2024
Statut:
epublish
Résumé
Common scab (CS) is a major bacterial disease causing lesions on potato tubers, degrading their appearance and reducing their market value. To accurately grade scab-infected potato tubers, this study introduces "ScabyNet", an image processing approach combining color-morphology analysis with deep learning techniques. ScabyNet estimates tuber quality traits and accurately detects and quantifies CS severity levels from color images. It is presented as a standalone application with a graphical user interface comprising two main modules. One module identifies and separates tubers on images and estimates quality-related morphological features. In addition, it enables the extraction of tubers as standard tiles for the deep-learning module. The deep-learning module detects and quantifies the scab infection into five severity classes related to the relative infected area. The analysis was performed on a dataset of 7154 images of individual tiles collected from field and glasshouse experiments. Combining the two modules yields essential parameters for quality and disease inspection. The first module simplifies imaging by replacing the region proposal step of instance segmentation networks. Furthermore, the approach is an operational tool for an affordable phenotyping system that selects scab-resistant genotypes while maintaining their market standards.
Identifiants
pubmed: 38218867
doi: 10.1038/s41598-023-51074-4
pii: 10.1038/s41598-023-51074-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1277Subventions
Organisme : The Research Council of Norway, The research funds for agriculture and food industry
ID : 294756
Informations de copyright
© 2024. The Author(s).
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