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

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

© 2020 The Authors. New Phytologist © 2020 New Phytologist Trust.

Bibliographische Detailangaben
Veröffentlicht in:The New phytologist. - 1979. - 228(2020), 2 vom: 01. Okt., Seite 778-793
1. Verfasser: Colmer, Joshua (VerfasserIn)
Weitere Verfasser: O'Neill, Carmel M, Wells, Rachel, Bostrom, Aaron, Reynolds, Daniel, Websdale, Danny, Shiralagi, Gagan, Lu, Wei, Lou, Qiaojun, Le Cornu, Thomas, Ball, Joshua, Renema, Jim, Flores Andaluz, Gema, Benjamins, Rene, Penfield, Steven, Zhou, Ji
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2020
Zugriff auf das übergeordnete Werk:The New phytologist
Schlagworte:Journal Article Research Support, Non-U.S. Gov't big data biology crop seeds germination scoring machine learning phenotypic analysis seed germination seed imaging Abscisic Acid 72S9A8J5GW
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520 |a 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 
650 4 |a Journal Article 
650 4 |a Research Support, Non-U.S. Gov't 
650 4 |a big data biology 
650 4 |a crop seeds 
650 4 |a germination scoring 
650 4 |a machine learning 
650 4 |a phenotypic analysis 
650 4 |a seed germination 
650 4 |a seed imaging 
650 7 |a Abscisic Acid  |2 NLM 
650 7 |a 72S9A8J5GW  |2 NLM 
700 1 |a O'Neill, Carmel M  |e verfasserin  |4 aut 
700 1 |a Wells, Rachel  |e verfasserin  |4 aut 
700 1 |a Bostrom, Aaron  |e verfasserin  |4 aut 
700 1 |a Reynolds, Daniel  |e verfasserin  |4 aut 
700 1 |a Websdale, Danny  |e verfasserin  |4 aut 
700 1 |a Shiralagi, Gagan  |e verfasserin  |4 aut 
700 1 |a Lu, Wei  |e verfasserin  |4 aut 
700 1 |a Lou, Qiaojun  |e verfasserin  |4 aut 
700 1 |a Le Cornu, Thomas  |e verfasserin  |4 aut 
700 1 |a Ball, Joshua  |e verfasserin  |4 aut 
700 1 |a Renema, Jim  |e verfasserin  |4 aut 
700 1 |a Flores Andaluz, Gema  |e verfasserin  |4 aut 
700 1 |a Benjamins, Rene  |e verfasserin  |4 aut 
700 1 |a Penfield, Steven  |e verfasserin  |4 aut 
700 1 |a Zhou, Ji  |e verfasserin  |4 aut 
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773 1 8 |g volume:228  |g year:2020  |g number:2  |g day:01  |g month:10  |g pages:778-793 
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