From Phenoscope to GreenLab model of Arabidopsis to decipher genotype and treatment effects
Abstract
Arabidopsis thaliana leaf development was tracked dynamically from high-throughput phenotyping image series using the SAM-2 deep learning model, enabling automated segmentation and tracking of individual leaves across hundreds of plants from different accessions grown under different water and nitrogen conditions. From these time series, leaf-level growth dynamics were reconstructed and used to calculate key developmental traits, including phyllochron and relative expansion rates. These measurements were further integrated into the GreenLab model to infer plant-scale parameters such as radiation use efficiency (RUE) and leaf demand parameters. Statistical analyses revealed significant genotype, treatment, and interaction effects on several traits, with stable genotype rankings but contrasted sensitivities to environmental conditions. While whole-rosette growth captured strong and consistent responses, individual leaf contributions to global growth remained limited, highlighting the integrative nature of rosette-scale dynamics. Model-derived parameters provided additional insight into plant strategies, showing that genotypes differ in both the duration and timing of leaf demand, with generally stable patterns across environments. In parallel, QTL mapping was performed using leaf-level measurements and phyllochron traits. This analysis identified shared and trait-specific genetic loci, including loci associated with leaf emergence dynamics that were not detected using traditional rosette-scale integrative traits. Overall, this study demonstrates that combining deep learning-based image analysis with mechanistic modeling enables large-scale and quantitative characterization of plant development, providing biologically interpretable traits and new insights into their genetic and environmental determinism. It highlights the potential of combining high-throughput image-based phenotyping, deep learning, and mechanistic modeling to generate biologically interpretable traits and to assess genetic and environmental influences on these traits.
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BibTeX
@article{bacave2026from,
title = {{From Phenoscope to GreenLab model of Arabidopsis to decipher genotype and treatment effects}},
author = {Hanna Bacave and Paul Huguet and Elodie Gilbault and Edgar Belin and Olivier Zurfluh and Olivier Loudet and Veronique Letort and Anne Goelzer},
journal = {Plant Phenomics},
year = {2026},
month = sep,
doi = {10.1016/j.plaphe.2026.100265},
url = {https://hal.inrae.fr/hal-05611239v2},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - From Phenoscope to GreenLab model of Arabidopsis to decipher genotype and treatment effects
AU - Hanna Bacave
AU - Paul Huguet
AU - Elodie Gilbault
AU - Edgar Belin
AU - Olivier Zurfluh
AU - Olivier Loudet
AU - Veronique Letort
AU - Anne Goelzer
PY - 2026
DA - 2026/09/01
JO - Plant Phenomics
DO - 10.1016/j.plaphe.2026.100265
UR - https://hal.inrae.fr/hal-05611239v2
AB - Arabidopsis thaliana leaf development was tracked dynamically from high-throughput phenotyping image series using the SAM-2 deep learning model, enabling automated segmentation and tracking of individual leaves across hundreds of plants from different accessions grown under different water and nitrogen conditions. From these time series, leaf-level growth dynamics were reconstructed and used to calculate key developmental traits, including phyllochron and relative expansion rates. These measurements were further integrated into the GreenLab model to infer plant-scale parameters such as radiation use efficiency (RUE) and leaf demand parameters. Statistical analyses revealed significant genotype, treatment, and interaction effects on several traits, with stable genotype rankings but contrasted sensitivities to environmental conditions. While whole-rosette growth captured strong and consistent responses, individual leaf contributions to global growth remained limited, highlighting the integrative nature of rosette-scale dynamics. Model-derived parameters provided additional insight into plant strategies, showing that genotypes differ in both the duration and timing of leaf demand, with generally stable patterns across environments. In parallel, QTL mapping was performed using leaf-level measurements and phyllochron traits. This analysis identified shared and trait-specific genetic loci, including loci associated with leaf emergence dynamics that were not detected using traditional rosette-scale integrative traits. Overall, this study demonstrates that combining deep learning-based image analysis with mechanistic modeling enables large-scale and quantitative characterization of plant development, providing biologically interpretable traits and new insights into their genetic and environmental determinism. It highlights the potential of combining high-throughput image-based phenotyping, deep learning, and mechanistic modeling to generate biologically interpretable traits and to assess genetic and environmental influences on these traits.
SN - 2643-6515
ER -