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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月11日

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  • Félix Mercier,
  • Geoffroy Couasnet,
  • Angelina El Ghaziri,
  • Nizar Bouhlel,
  • Alain Sarniguet,
  • Muriel Marchi,
  • Matthieu Barret,
  • David Rousseau

摘要

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.