Quantum-inspired phenotyping: a new paradigm for dynamic trait characterization and gene discovery in crops.

Abstract

Abstract Crop improvement increasingly depends on resolving how plants move through physiological states during stress rather than where they end up, yet the dominant convention still reduces dense, multi-sensor, longitudinal trait data to a single static value before genetic analysis, discarding information about state, transition and uncertainty. This systematic review asked whether a quantum-inspired, state-based representation of dynamic crop phenotypes could be integrated with established genomic tools to improve trait characterisation and gene discovery, and what the literature reports about the components it would require. Reporting followed the PRISMA 2020 statement and the Synthesis Without Meta-analysis (SWiM) guideline. Scopus, Web of Science Core Collection, PubMed and a Google Scholar grey-literature sweep were searched from January 2017, retrieving 2,413 records, after de-duplication 1,777 titles and abstracts were screened, 279 full texts were assessed, and 82 studies met the eligibility criteria and entered a thematic synthesis. Studies were dual-screened, appraised with an adapted Mixed Methods Appraisal Tool, and their comparable within-study outcomes synthesised by vote counting on direction of effect; meta-analysis was inappropriate because outcomes were not commensurable. Every component of the paradigm probabilistic state representation, temporal trait modelling and trajectory-aware genomic prediction was independently validated, but no included study unified them for crop-stress genetics. All comparable comparisons favoured the temporally richer method, an asymmetry indicating probable reporting bias, and certainty was moderate for representation and modelling and low for realised genetic gain. The phenotyping bottleneck has migrated from measurement to representation.

Plant phenotyping relevance

作物の動的表現型を状態表現・時間的形質モデル・軌跡対応ゲノム予測で扱う方法論の系統的レビューであり、表現型の表現・解析手法が中心である。

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