Tools
Tools
Colmer, Joshua, O'Neill, Carmel M., Wells, Rachel, Bostrom, Aaron 
ORCID: https://orcid.org/0000-0002-7300-6038, Reynolds, Daniel, Websdale, Danny, Shiralagi, Gagan, Lu, Wei, Lou, Qiaojun, Le Cornu, Thomas, Ball, Joshua, Renema, Jim, Flores Andaluz, Gema, Benjamins, Rene, Penfield, Steven and Zhou, Ji
(2020)
SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination.
New Phytologist, 228 (2).
pp. 778-793.
ISSN 0028-646X
Preview |
PDF (Accepted_Manuscript)
- Accepted Version
Available under License Creative Commons Attribution. Download (20MB) | Preview |
Abstract
Efficient seed germination and establishment are important traits for field and glasshouse crops. Large-scale germination experiments are laborious and prone to observer errors, leading to the necessity for automated methods. We experimented with five crop species, including tomato, pepper, Brassica, barley, and maize, and concluded an approach for large-scale germination scoring. Here, we present the SeedGerm system, which combines cost-effective hardware and open-source software for seed germination experiments, automated seed imaging, and machine-learning based phenotypic analysis. The software can process multiple image series simultaneously and produce reliable analysis of germination- and establishment-related traits, in both comma-separated values (CSV) and processed images (PNG) formats. In this article, we describe the hardware and software design in detail. We also demonstrate that SeedGerm could match specialists’ scoring of radicle emergence. Germination curves were produced based on seed-level germination timing and rates rather than a fitted curve. In particular, by scoring germination across a diverse panel of Brassica napus varieties, SeedGerm implicates a gene important in abscisic acid (ABA) signalling in seeds. We compared SeedGerm with existing methods and concluded that it could have wide utilities in large-scale seed phenotyping and testing, for both research and routine seed technology applications.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | big data biology,crop seeds,germination scoring,machine learning,phenotypic analysis,seed germination,seed imaging,physiology,plant science ,/dk/atira/pure/subjectarea/asjc/1300/1314 |
| Faculty \ School: | Faculty of Science > School of Biological Sciences Faculty of Science > School of Computing Sciences Faculty of Science |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 02 Jul 2020 23:58 |
| Last Modified: | 23 Oct 2024 23:54 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/75883 |
| DOI: | 10.1111/nph.16736 |
Downloads
Downloads per month over past year
Actions (login required)
 |
View Item |