Automated dynamic phenotyping of whole oilseed rape (Brassica napus) plants from images collected under controlled conditions
This highlights the potential applicability of the MapReader model framework to images data from across scientific and humanities domains, and the flexibility it provides in creating pipelines with different modelling approaches. The pipeline for dynamic plant phenotyping from whole plant images developed in this study could potentially be applied to imagery from varied laboratory conditions, and to images datasets of other plants of both agricultural and conservation concern.
Authors
- Smita Kurup (ORCID: https://orcid.org/0000-0003-1035-7065)
- Laura Siles (ORCID: https://orcid.org/0000-0003-2264-9436)
- Evangeline Corcoran (ORCID: https://orcid.org/0000-0003-0321-009X)
- Kasra Hosseini (ORCID: https://orcid.org/0000-0003-4396-6019)
- Sebastian E. Ahnert (ORCID: https://orcid.org/0000-0003-2613-0041)
Institutions
- University of Cambridge (GB)
- Rothamsted Research (GB)
- EuroPhoton (Germany) (DE)
- The Alan Turing Institute (GB)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-05-22
- DOI
- https://doi.org/10.3389/fpls.2025.1443882
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- UK Research and Innovation
- Aberystwyth University
- Engineering and Physical Sciences Research Council
- Biotechnology and Biological Sciences Research Council