Quantitative research from the perspective of mathematical and physical crop science: a review of phenotyping, mechanics, and modeling.

Abstract

Addressing global grand challenges, including food security, climate change, and resource scarcity, requires transcending the limitations of traditional crop science research. Traditional approaches often suffer from low-throughput, destructive nature, and qualitatively macroscopic analyses, hindering the in-depth exploration and precise manipulation of crop growth mechanisms necessary for modern agriculture. This review systematically synthesizes recent advancements and pinpoints critical bottlenecks in key areas of modern crop science research: high-throughput phenotyping, multiscale mechanics of crops, and numerical modeling of crop-environment interactions. Based on this synthesis, we propose and articulate a conceptual framework for the novel interdisciplinary field: “mathematical and physical crop science.” The framework establishes an integrated paradigm of “data-driven, mechanism-based, and system-predictive” research, structured as follows: (1) High-throughput phenotyping, coupled with artificial intelligence and machine learning-driven analysis, quantifies dynamic phenotypic traits emerging from genotype-by-environment interactions. (2) Multiscale mechanics of crops resolves the physical constraints governing crop structure and function across different scales. (3) Numerical modeling of crop-environment interactions simulates the dynamic interactions between crop physiological processes and environmental factors. The overarching goal is to integrate these historically disparate research domains, providing a unified theoretical foundation for the systematic understanding of crop physiological and developmental processes and informing sustainable agricultural practices.

Plant phenotyping relevance

高スループット植物フェノタイピングを主要領域として体系的にレビューし、AI・機械学習による形質定量を扱うため、フェノタイピング方法レビューに該当する。

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