Article Dans Une Revue Plant Methods Année : 2025

Deep-learning-ready RGB-depth images of seedling development

Résumé

In the era of machine learning-driven plant imaging, the production of annotated datasets is a very important contribution. In this data paper, a unique annotated dataset of seedling emergence kinetics is proposed. It is composed of almost 70,000 RGB-depth frames and more than 700,000 plant annotations. The dataset is shown valuable for training deep learning models and performing high-throughput phenotyping by imaging. The ability of such models to generalize to several species and outperform the state-of-the-art owing to the delivered dataset is demonstrated. We also discuss how this dataset raises new questions in plant phenotyping.
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hal-04948208 , version 1 (14-02-2025)

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Félix Mercier, Geoffroy Couasnet, Angelina El Ghaziri, Nizar Bouhlel, Alain Sarniguet, et al.. Deep-learning-ready RGB-depth images of seedling development. Plant Methods, 2025, 21, 14 p. ⟨10.1186/s13007-025-01334-3⟩. ⟨hal-04948208⟩
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