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

IF 4.4 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS Plant Methods Pub Date : 2025-02-11 DOI:10.1186/s13007-025-01334-3

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

引用次数: 0

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.

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幼苗发育的深度学习rgb深度图像。

在机器学习驱动的植物成像时代，带注释数据集的产生是一个非常重要的贡献。本文提出了一个独特的幼苗出苗动力学注释数据集。它由近70,000个rgb深度帧和超过700,000个植物注释组成。该数据集对于训练深度学习模型和通过成像执行高通量表型有价值。这种模型能够推广到几个物种，并且由于交付的数据集而优于最先进的技术。我们还讨论了该数据集如何在植物表型中提出新的问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Plant Methods 生物-植物科学

CiteScore

9.20

自引率

3.90%

发文量

121

审稿时长

2 months

期刊介绍： Plant Methods is an open access, peer-reviewed, online journal for the plant research community that encompasses all aspects of technological innovation in the plant sciences. There is no doubt that we have entered an exciting new era in plant biology. The completion of the Arabidopsis genome sequence, and the rapid progress being made in other plant genomics projects are providing unparalleled opportunities for progress in all areas of plant science. Nevertheless, enormous challenges lie ahead if we are to understand the function of every gene in the genome, and how the individual parts work together to make the whole organism. Achieving these goals will require an unprecedented collaborative effort, combining high-throughput, system-wide technologies with more focused approaches that integrate traditional disciplines such as cell biology, biochemistry and molecular genetics. Technological innovation is probably the most important catalyst for progress in any scientific discipline. Plant Methods’ goal is to stimulate the development and adoption of new and improved techniques and research tools and, where appropriate, to promote consistency of methodologies for better integration of data from different laboratories.