Deep-learning-ready RGB-depth images of seedling development - Mercier, Félix; Couasnet, Geoffroy; El Ghaziri, Angelina; Bouhlel, Nizar; Sarniguet, Alain; Marchi, Muriel; Barret, Matthieu; Rousseau, David (Plant methods, 2025)
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.