Unlocking the power of AI for phenotyping fruit morphology in Arabidopsis

Unlocking the power of AI for phenotyping fruit morphology in Arabidopsis

Deep learning can revolutionise high-throughput image-based phenotyping by automating the measurement of complex traits, a task that is often la bour-intensi v e, time-consuming, and prone to human err or. Howev er, its pr ecision and adapta bility in accuratel y phenotyping organ-level tr aits, suc...

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Detalles Bibliográficos
Autores principales: Atkins, Kieran, Garzón Martínez, Gina A., Lloyd, Andrew, Doonan, John H., Chuan, Lu
Formato: article
Lenguaje:Inglés
Publicado: Oxford University Press 2025
Materias:
plant phenotyping
Arabidopsis
fruit morphology
instance segmentation
deep learning
QTL analysis
MAGIC population
Arabidopsi
Inteligencia artificial
Morfología vegetal
Fenotipado
Transversal
http://aims.fao.org/aos/agrovoc/c_33291
http://aims.fao.org/aos/agrovoc/c_27064
http://aims.fao.org/aos/agrovoc/c_13434
http://aims.fao.org/aos/agrovoc/c_45113221
Acceso en línea:https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giae123/8010444
http://hdl.handle.net/20.500.12324/41152
https://doi.org/10.1093/gigascience/giae123

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