Hypocotyl length determination is a widely used method to phenotype young seedlings. The measurement itself has advanced from using rulers and millimeter papers to assessing digitized images but remains a labor-intensive, monotonous, and time-consuming procedure. To make high-throughput plant phenotyping possible, we developed a deep-learning-based approach to simplify and accelerate this method. Our pipeline does not require a specialized imaging system but works well with low-quality images produced with a simple flatbed scanner or a smartphone camera. Moreover, it is easily adaptable for a diverse range of datasets not restricted to Arabidopsis (Arabidopsis thaliana). Furthermore, we show that the accuracy of the method reaches human performance. We not only provide the full code at https://github.com/biomag-lab/hypocotyl-UNet, but also give detailed instructions on how the algorithm can be trained with custom data, tailoring it for the requirements and imaging setup of the user.

中文翻译：

---

基于深度学习的高通量下胚轴表型分析方法。

下胚轴长度测定是一种广泛用于幼苗表型分析的方法。测量本身已经从使用尺子和毫米纸发展到评估数字化图像，但仍然是一个劳动密集型、单调且耗时的过程。为了使高通量植物表型分析成为可能，我们开发了一种基于深度学习的方法来简化和加速该方法。我们的管道不需要专门的成像系统，但可以很好地处理使用简单的平板扫描仪或智能手机相机生成的低质量图像。此外，它可以轻松适应各种数据集，而不仅限于拟南芥（Arabidopsis thaliana）。此外，我们表明该方法的准确性达到了人类的水平。我们不仅在 https://github.com/biomag-lab/hypocotyl-UNet 上提供完整的代码，而且还提供了如何使用自定义数据训练算法的详细说明，并根据系统的要求和成像设置进行定制。用户。