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科学领域:

  • 植物科学 植物科学
  • 农业技术 农业技术
  • 计算机视觉 计算机视觉

背景情况:

  • 微微基子子 (MR) 成像对于了解环境变化在现场的根反应至关重要.
  • 手动分析MRI图像是劳动密集型的,并且限制了高通量表型.
  • 需要自动化根分析来加速研究和支持精准农业.

研究的目的:

  • 适应和评估卷积神经网络 (CNN) 模型,以直接从微观图像中估计总根长度 (TRL).
  • 为了比较基于回归和基于检测的CNN模型对TRL估计的性能.
  • 为各种作物物种的根系表型提供一个强大而有效的工具.

主要方法:

  • 开发和调整CNN模型用于TRL估计,绕过图像细分.
  • 使用Rootfly软件的手册注释来训练数据.
  • 通过使用两个MR系统,对四种作物 (玉米,胡,西瓜,西红) 的4015张图像进行了训练和验证.

主要成果:

  • 与手动测量相比,CNN模型在估计TRL方面取得了很高的准确性 (R2 = 0.9290.986).
  • 基于检测的模型有助于对根注释进行视觉检查.
  • 模型在不同的MR系统,作物物种和图像质量中展示了强度.

结论:

  • 开发的基于CNN的工具准确有效地估计了MR图像的TRL,克服了手动分析的局限性.
  • 这项技术通过实时监控根生长,支持精准农业.
  • 公共可用的数据集和模型将促进根类表型的进一步进步.