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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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

id	RepoAGROSAVIA41152
record_format	dspace
institution	Corporación Colombiana de Investigación Agropecuaria
collection	Repositorio AGROSAVIA
language	Inglés
topic	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
spellingShingle	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 Atkins, Kieran Garzón Martínez, Gina A. Lloyd, Andrew Doonan, John H. Chuan, Lu Unlocking the power of AI for phenotyping fruit morphology in Arabidopsis
description	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 h as fruit morphology, remain to be fully evaluated. Establishing the links between phenotypic and genotypic variation is essential for uncovering the genetic basis of traits and can also provide an orthologous test of pipeline effecti v eness. In this study, we assess the efficacy of deep learning for measuring variation in fruit morphology in Arabidopsis using ima ges fr om a m ultipar ent adv anced gener ation inter cr oss (MAGIC) mapping famil y. We tr ained an instance se gmentation model and developed a pipeline to phenotype Arabidopsis fruit morphology, based on the model outputs. Our model achieved strong perfor- mance with an av era ge pr ecision of 88.0% for detection and 55.9% for segmentation. Quantitati v e trait locus analysis of the derived phenotypic metrics of the MAGIC population identified significant loci associated with fruit morphology. This analysis, based on au- tomated phenotyping of 332,194 individual fruits, underscores the capability of deep learning as a robust tool for phenotyping large populations. Our pipeline for quantifying pod morphological traits is scala b le and provides high-quality phenotype data, facilitating genetic analysis and gene discov er y, as well as advancing crop breeding resear c h.
format	article
author	Atkins, Kieran Garzón Martínez, Gina A. Lloyd, Andrew Doonan, John H. Chuan, Lu
author_facet	Atkins, Kieran Garzón Martínez, Gina A. Lloyd, Andrew Doonan, John H. Chuan, Lu
author_sort	Atkins, Kieran
title	Unlocking the power of AI for phenotyping fruit morphology in Arabidopsis
title_short	Unlocking the power of AI for phenotyping fruit morphology in Arabidopsis
title_full	Unlocking the power of AI for phenotyping fruit morphology in Arabidopsis
title_fullStr	Unlocking the power of AI for phenotyping fruit morphology in Arabidopsis
title_full_unstemmed	Unlocking the power of AI for phenotyping fruit morphology in Arabidopsis
title_sort	unlocking the power of ai for phenotyping fruit morphology in arabidopsis
publisher	Oxford University Press
publishDate	2025
url	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
work_keys_str_mv	AT atkinskieran unlockingthepowerofaiforphenotypingfruitmorphologyinarabidopsis AT garzonmartinezginaa unlockingthepowerofaiforphenotypingfruitmorphologyinarabidopsis AT lloydandrew unlockingthepowerofaiforphenotypingfruitmorphologyinarabidopsis AT doonanjohnh unlockingthepowerofaiforphenotypingfruitmorphologyinarabidopsis AT chuanlu unlockingthepowerofaiforphenotypingfruitmorphologyinarabidopsis
_version_	1842255962396688384
spelling	RepoAGROSAVIA411522025-08-30T03:01:33Z Unlocking the power of AI for phenotyping fruit morphology in Arabidopsis Unlocking the power of AI for phenotyping fruit morphology in Arabidopsis Atkins, Kieran Garzón Martínez, Gina A. Lloyd, Andrew Doonan, John H. Chuan, Lu 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 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 h as fruit morphology, remain to be fully evaluated. Establishing the links between phenotypic and genotypic variation is essential for uncovering the genetic basis of traits and can also provide an orthologous test of pipeline effecti v eness. In this study, we assess the efficacy of deep learning for measuring variation in fruit morphology in Arabidopsis using ima ges fr om a m ultipar ent adv anced gener ation inter cr oss (MAGIC) mapping famil y. We tr ained an instance se gmentation model and developed a pipeline to phenotype Arabidopsis fruit morphology, based on the model outputs. Our model achieved strong perfor- mance with an av era ge pr ecision of 88.0% for detection and 55.9% for segmentation. Quantitati v e trait locus analysis of the derived phenotypic metrics of the MAGIC population identified significant loci associated with fruit morphology. This analysis, based on au- tomated phenotyping of 332,194 individual fruits, underscores the capability of deep learning as a robust tool for phenotyping large populations. Our pipeline for quantifying pod morphological traits is scala b le and provides high-quality phenotype data, facilitating genetic analysis and gene discov er y, as well as advancing crop breeding resear c h. National Capability in Plant Phenotyping FoodBioSystems Doctoral Training Partnership - DTP 2025-08-29T16:41:33Z 2025-08-29T16:41:33Z 2025-02 2025 article Artículo científico http://purl.org/coar/resource_type/c_2df8fbb1 info:eu-repo/semantics/article https://purl.org/redcol/resource_type/ART http://purl.org/coar/version/c_970fb48d4fbd8a85 https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giae123/8010444 2047-217X http://hdl.handle.net/20.500.12324/41152 https://doi.org/10.1093/gigascience/giae123 reponame:Biblioteca Digital Agropecuaria de Colombia instname:Corporación colombiana de investigación agropecuaria AGROSAVIA eng GigaScience 14 1 1 14 Marsh JI, Hu H, Gill M, et al. 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Unlocking the power of AI for phenotyping fruit morphology in Arabidopsis

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