pyRootHair: Machine learning accelerated software for high-throughput phenotyping of plant root hair traits
The application of pyRootHair enables users to rapidly screen a large number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI (https://pypi.org/project/pyRootHair/) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair.
Authors
- Jonathan A. Atkinson (ORCID: https://orcid.org/0000-0003-2815-0812)
- Fiona Leigh (ORCID: https://orcid.org/0000-0002-3221-5327)
- Ian Tsang (ORCID: https://orcid.org/0009-0006-1999-3251)
- James Cockram (ORCID: https://orcid.org/0000-0002-1014-6463)
- Lawrence Percival‐Alwyn (ORCID: https://orcid.org/0000-0001-7725-9203)
- Stephen Rawsthorne
Institutions
- University of Nottingham (GB)
- National Institute of Agricultural Botany (GB)
- Morpho (United States) (US)
- Morgan Stanley (United States) (US)
Publication Details
- Journal
- GigaScience
- Published
- 2025-11-12
- DOI
- https://doi.org/10.1093/gigascience/giaf141
- Primary Topic
- Plant Molecular Biology Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00