RotatedStomataNet: a deep rotated object detection network for directional stomata phenotype analysis.
Aperture
Computer vision
Phenotypic analysis
Rotated object detection
Stoma
Journal
Plant cell reports
ISSN: 1432-203X
Titre abrégé: Plant Cell Rep
Pays: Germany
ID NLM: 9880970
Informations de publication
Date de publication:
23 Apr 2024
23 Apr 2024
Historique:
received:
28
11
2023
accepted:
02
01
2024
medline:
23
4
2024
pubmed:
23
4
2024
entrez:
23
4
2024
Statut:
epublish
Résumé
Innovatively, we consider stomatal detection as rotated object detection and provide an end-to-end, batch, rotated, real-time stomatal density and aperture size intelligent detection and identification system, RotatedeStomataNet. Stomata acts as a pathway for air and water vapor in the course of respiration, transpiration, and other gas metabolism, so the stomata phenotype is important for plant growth and development. Intelligent detection of high-throughput stoma is a key issue. Nevertheless, currently available methods usually suffer from detection errors or cumbersome operations when facing densely and unevenly arranged stomata. The proposed RotatedStomataNet innovatively regards stomata detection as rotated object detection, enabling an end-to-end, real-time, and intelligent phenotype analysis of stomata and apertures. The system is constructed based on the Arabidopsis and maize stomatal data sets acquired destructively, and the maize stomatal data set acquired in a non-destructive way, enabling the one-stop automatic collection of phenotypic, such as the location, density, length, and width of stomata and apertures without step-by-step operations. The accuracy of this system to acquire stomata and apertures has been well demonstrated in monocotyledon and dicotyledon, such as Arabidopsis, soybean, wheat, and maize. The experimental results that the prediction results of the method are consistent with those of manual labeling. The test sets, the system code, and their usage are also given ( https://github.com/AITAhenu/RotatedStomataNet ).
Identifiants
pubmed: 38652181
doi: 10.1007/s00299-024-03149-3
pii: 10.1007/s00299-024-03149-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
126Subventions
Organisme : Natural Science Foundation of Henan Province
ID : 222300420417
Organisme : the National Natural Science Foundation of China
ID : 31970808
Organisme : the Program for Innovative Research Team (in Science and Technology) at University of Henan Province
ID : 21IRTSTHN019
Organisme : the Key Scientific Research Project in Colleges and Universities of Henan Province of China
ID : 22A110002
Organisme : Henan Provincial Joint Science and Technology R&D Project
ID : 222301420103
Organisme : Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City
ID : 2021JJLH0087
Organisme : Natural Science Foundation for Young Scientists of Shanxi Province
ID : 62272313
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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