文献类型： 外文期刊

作者： Colmer, Joshua ¹ ; O'Neill, Carmel M. ² ; Wells, Rachel ² ; Bostrom, Aaron ¹ ; Reynolds, Daniel ¹ ; Websdale, Danny ¹ ;

作者机构： 1.Earlham Inst, Engn Biol, Norwich Res Pk, Norwich NR4 7UZ, Norfolk, England

2.John Innes Ctr, Crop Genet, Norwich Res Pk, Norwich NR4 7UH, Norfolk, England

3.Nanjing Agr Univ, Coll Engn, Nanjing 210095, Jiangsu, Peoples R China

4.Shanghai Acad Agr Sci, Shanghai Agrobiol Gene Ctr, Shanghai 201106, Peoples R China

5.Syngenta Seeds BV, NL-1601 BK Enkhuizen, Netherlands

6.Nanjing Agr Univ, Plant Phen Res Ctr, Jiangsu Collaborat Innovat Ctr Modern Crop Prod, State Key Lab Crop Genet & Germplasm Enhancement, Nanjing 210095, Peoples R China

7.Natl Inst Agr Bot, Cambridge Crop Res, Cambridge CB3 0LE, England

关键词： big data biology; crop seeds; germination scoring; machine learning; phenotypic analysis; seed germination; seed imaging

期刊名称：NEW PHYTOLOGIST （ 影响因子：10.151； 五年影响因子：10.475 ）

ISSN： 0028-646X

年卷期： 2020 年 228 卷 2 期

页码：

收录情况： SCI

摘要： 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 ofBrassica napusvarieties, 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.