Seed imaging omics: A bridge from perception to cognition for the future of seed phenotyping

Abstract

Seeds are complex living systems that display rich diversity in morphology, physiology, biochemistry, and genetics. Yet phenotyping during seed dormancy remains hampered by limited imaging modalities, insufficient data integration, and underpowered intelligent analytics—constraints that impede the efficiency and accuracy of precision breeding and germplasm evaluation. In the AI-for-Science era, seed phenomics research urgently needs to establish an end-to-end “measure–compute–understand–apply” pipeline, spanning cross-scale multimodal data acquisition to insight. This article systematically reviews the technical evolution of dormancy-state seed phenotyping and delineates five stages—manual observation phenotypes, biochemical phenotypes, image-based phenotypes, digital seeds, and intelligent seeds—summarizing the defining features and principal limitations of each. The deep integration of advanced imaging with artificial intelligence offers new opportunities to overcome existing bottlenecks. Seed Imaging Omics has emerged to meet this need: leveraging multiscale, multidimensional imaging for comprehensive observation and multimodal data capture; coupling these data with multimodal fusion, foundation-model analysis, and virtual seed reconstruction to enable precise feature extraction and pattern discovery from large image corpora. These capabilities clarify complex traits, reveal morphology–function relationships, and advance systems-level understanding of seed biology, ultimately supporting precise germplasm management and evaluation, data-driven elucidation of biological mechanisms, and accelerated innovation in crop improvement. Looking ahead, continued progress in sensing and imaging, foundation models, and large-scale analytics will drive seed phenotyping toward “intelligent” systems capable of autonomous sensing, real-time analysis, and decision-making across the seed life cycle—transforming seeds from passive carriers of genetic and phenotypic information into smart units that integrate phenotypic logging, state monitoring, performance assessment, and management feedback.

Plant phenotyping relevance

種子休眠状態の表現型計測技術を体系的にレビューし、画像取得、マルチモーダル統合、特徴抽出、AI解析を中心に扱うため、植物フェノタイピング手法レビューとして含める。

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