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

Abstract

Summary 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.

Plant phenotyping relevance

種子発芽・定着形質の画像取得と機械学習による自動抽出を行うハードウェア／ソフトウェア基盤を詳細に開発・検証しており、フェノタイピング手法が研究の中心である。

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