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

Abstract

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.

Plant phenotyping relevance

植物の出芽速度を対象とする大規模RGB深度画像・アノテーションデータセットを提供し、深層学習および高スループット表現型解析への利用性を実証しており、表現型取得基盤が中心である。

Code and data availability

This is a data paper whose core contribution is a public annotated RGB-depth seedling dataset (~70,000 frames, >700,000 annotations) deposited in DATA INRAE with DOI 10.57745/AMFJTK, explicitly stated as publicly accessible. Other allowed URLs (license, Intel datasheet, Jülich record) are not paper-specific assets.