A deep learning-based approach for high-throughput hypocotyl phenotyping

Orsolya Dobos, Peter Horvath, Ferenc Nagy, Tivadar Danka, András Viczián

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1415-1424
Number of pages10
JournalPlant physiology
Volume181
Issue number4
DOIs
Publication statusPublished - Jan 1 2019

ASJC Scopus subject areas

  • Physiology
  • Genetics
  • Plant Science

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