A simple method for measuring pollen germination rate using machine learning.
Abiotic stress
Chili pepper
Microscope
Object detection
Yolov5
Journal
Plant reproduction
ISSN: 2194-7961
Titre abrégé: Plant Reprod
Pays: Germany
ID NLM: 101602701
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
25
08
2022
accepted:
30
05
2023
medline:
23
10
2023
pubmed:
6
6
2023
entrez:
6
6
2023
Statut:
ppublish
Résumé
The pollen germination rate decreases under various abiotic stresses, such as high-temperature stress, and it is one of the causes of inhibition of plant reproduction. Thus, measuring pollen germination rate is vital for understanding the reproductive ability of plants. However, measuring the pollen germination rate requires much labor when counting pollen. Therefore, we used the Yolov5 machine learning package in order to perform transfer learning and constructed a model that can detect germinated and non-germinated pollen separately. Pollen images of the chili pepper, Capsicum annuum, were used to create this model. Using images with a width of 640 pixels for training constructed a more accurate model than using images with a width of 320 pixels. This model could estimate the pollen germination rate of the F
Identifiants
pubmed: 37278944
doi: 10.1007/s00497-023-00472-9
pii: 10.1007/s00497-023-00472-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
355-364Subventions
Organisme : Kaken Pharmaceutical
ID : 21K05575
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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