MultipleXLab: A high-throughput portable live-imaging root phenotyping platform using deep learning and computer vision

Vinicius Lube, Mehmet Alican Noyan, Alexander Przybysz, Khaled Salama, Ikram Blilou*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Background: Profiling the plant root architecture is vital for selecting resilient crops that can efficiently take up water and nutrients. The high-performance imaging tools available to study root-growth dynamics with the optimal resolution are costly and stationary. In addition, performing nondestructive high-throughput phenotyping to extract the structural and morphological features of roots remains challenging. Results: We developed the MultipleXLab: a modular, mobile, and cost-effective setup to tackle these limitations. The system can continuously monitor thousands of seeds from germination to root development based on a conventional camera attached to a motorized multiaxis-rotational stage and custom-built 3D-printed plate holder with integrated light-emitting diode lighting. We also developed an image segmentation model based on deep learning that allows the users to analyze the data automatically. We tested the MultipleXLab to monitor seed germination and root growth of Arabidopsis developmental, cell cycle, and auxin transport mutants non-invasively at high-throughput and showed that the system provides robust data and allows precise evaluation of germination index and hourly growth rate between mutants. Conclusion: MultipleXLab provides a flexible and user-friendly root phenotyping platform that is an attractive mobile alternative to high-end imaging platforms and stationary growth chambers. It can be used in numerous applications by plant biologists, the seed industry, crop scientists, and breeding companies.

Original languageEnglish (US)
Article number38
JournalPlant Methods
Volume18
Issue number1
DOIs
StatePublished - Dec 2022

Bibliographical note

Funding Information:
KAUST provided the funding to undertake this research under Baseline Research Funds (BAS/1/1081-01-01) and Research Translational Funds (REI/1/4584‐01‐01).

Publisher Copyright:
© 2022, The Author(s).

Keywords

  • Automation
  • CNC microscope
  • Image segmentation
  • Machine learning
  • Phenomics

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Plant Science

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