Advancements in Plant Diagnostic and Sensing Technologies

Abstract

Abstract Recent advancements in plant sensing technologies have significantly improved agricultural productivity while reducing resource inputs, resulting in higher yields by enabling early disease detection, precise diagnostics, and optimized fertilizer and pesticide applications. Each adopted technology offers unique advantages suitable for various farm operations, breeding programs, and laboratory research. This review article first summarizes key target traits, endogenous structures, and metabolites that serve as focal points for plant diagnostic and sensing technologies. Next, conventional plant sensing technologies based on light reflectance and fluorescence, which rely on foliar phytopigments and fluorophores such as chlorophylls are discussed. These methods, along with advanced analytical strategies incorporating machine learning, enable accurate stress detection and classification beyond general assessments of plant health and stress status. Advanced optical techniques such as Fourier transform infrared spectroscopy (FT‐IR) and Raman spectroscopy, which allow specific measurements of various plant metabolites and structural components are then highlighted. Furthermore, the design and applications of nanotechnology chemical sensors capable of highly sensitive and selective detection of specific phytochemicals, including phytohormones and signaling second messengers, which regulate physiological and developmental processes at micro‐ to sub‐micromolar concentrations are introduced. By selecting appropriate sensing methodologies, agricultural production, and relevant research activities can be significantly improved.

Plant phenotyping relevance

植物の診断・センシング技術を体系的にレビューし、形質・構造・代謝物の測定、ストレス検出、光学・分光・化学センサーを中心に扱うため、方法論レビューとして対象範囲に該当する。

Code and data availability

This is a review article summarizing plant sensing technologies. All figures are either created with BioRender or reproduced from cited prior publications (e.g., refs 26, 47, 59) under CC BY licenses; no paper-specific phenotype datasets, sensor data, analysis code, or trained models are deposited or made available by,

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