Digital phenotyping as an integrative framework for engineering and life science approaches in agro-ecosystems

Abstract

Digital phenotyping has evolved from simple imaging-based trait measurement to a core technology enabling large-scale analysis of genotype × environment × management (G × E × M) interactions. This review highlights the manner in which digital phenotyping simultaneously supports two major research trajectories in modern agro-ecosystems. In engineering-driven approaches, imaging and sensor data are increasingly used to enhance the physical control of climate, irrigation, and crop protection through model-based and AI-assisted decision systems. In life science-centered approaches, high-throughput phenotyping and multi-omics integration provide mechanistic insights into plant stress responses, developmental plasticity, and complex trait regulation. Despite significant progress, both pathways face limitations in addressing the biological complexity and climatic unpredictability of crop systems. We discuss the emerging opportunities to integrate these domains through a two-layer AI framework that combines real-time sensing and actuation (“Physical AI”) with ontology- and LLM-based reasoning systems capable of synthesizing biological knowledge and generating strategic policies. Together, these developments position digital phenotyping as a bridging technology and a foundation for adaptive, resilient, and knowledge-driven agro-ecosystem management.

Plant phenotyping relevance

植物デジタルフェノタイピングを中心に、画像・センサーデータによる形質測定と高スループット解析の発展・応用をレビューしており、方法論レビューとして中心的である。

Code and data availability

This is a review article with no paper-specific phenotype datasets, images, code, or models. The only availability statement is 'The data are available from the corresponding author upon reasonable request,' which does not provide a public asset, and all URLs in the text are citations to prior work.

No evidence-backed public reproduction asset is currently recorded.