SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination

Joshua Colmer, Carmel M. O'Neill, Rachel Wells, Aaron Bostrom, Daniel Reynolds, Danny Websdale, Gagan Shiralagi, Wei Lu, Qiaojun Lou, Thomas Le Cornu, Joshua Ball, Jim Renema, Gema Flores Andaluz, Rene Benjamins, Steven Penfield, Ji Zhou

Research output: Contribution to journalArticle

12 Citations (Scopus)
17 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)778-793
Number of pages16
JournalNew Phytologist
Volume228
Issue number2
Early online date13 Jun 2020
DOIs
Publication statusPublished - Oct 2020

Keywords

  • big data biology
  • crop seeds
  • germination scoring
  • machine learning
  • phenotypic analysis
  • seed germination
  • seed imaging

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