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通过姿势估计进行快速有效的根系表型鉴定.

Elizabeth M Berrigan1, Lin Wang1, Hannah Carrillo1

  • 1Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America.

bioRxiv : the preprint server for biology
|December 4, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的深度学习方法,用于植物根表型,使用姿势估计来更快地准确地绘制根结构,并且比传统的细分方法更少的数据.

科学领域:

  • 植物生物学 植物生物学
  • 计算机视觉 计算机视觉 计算机视觉
  • 生物信息学是一种生物信息学.

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  • 传统的植物表型依赖于图像细分,这是劳动密集型和容易出错的.
  • 基于细分的方法需要大量的数据注释用于培训,并且很容易掩盖缺陷.

结论:

  • 位估计提供了一种更有效,更可靠的替代品,用于植物表型的细分.
  • 开发的工具 (sleap-roots) 和资源是公开可用的,以促进采用和进一步研究.