Advances in surface-enhanced Raman scattering applications for precision agriculture: monitoring plant health and crop quality.

Abstract

Ensuring plant health and crop quality is vital for sustainable modern agriculture. Conventional detection methods for stress markers, contaminants, and pathogens are often constrained by labor-intensive procedures, bulky equipment, and reliance on centralized facilities, limiting real-time field monitoring. Surface-enhanced Raman scattering (SERS) has emerged as a promising solution, providing rapid, ultrasensitive, and non-destructive analysis across plant, soil, and water matrices. This review outlines the fundamental SERS mechanisms and strategies that boost sensing performance, and surveys recent advances in monitoring throughout the cultivation cycle, covering plant stress markers, metabolites, contaminants, and plant pathogens under realistic agricultural conditions. Emphasis is placed on substrate architecture (hot-spot control, composites/heterostructures, functionalization, flexible formats), enhancement mechanisms, and analytical performance (typical enhancement factor (EF), limit of detection (LOD), limit of quantitation (LOQ), and relative standard deviation (RSD) ranges). Persistent challenges, including substrate reproducibility, matrix interference, quantitative calibration, and scalable fabrication for field deployment, are evaluated alongside emerging solutions, including matrix-aware calibration (with ratiometric readout), fluorescence-robust preprocessing, and durable, large-area platforms. We close with practical considerations for durability and cost and with future perspectives toward next-generation, field-ready SERS tools for proactive plant-health management and crop-quality assurance.

Plant phenotyping relevance

植物の健康状態やストレス指標を測定するSERSセンシング手法を中心に、基板設計、分析性能、校正、再現性、フィールド展開上の課題を体系的にレビューしており、植物フェノタイピング手法のレビューに該当する。

Code and data availability

This is a review article on SERS in precision agriculture. The authors explicitly state no new datasets or code were generated: 'This review does not generate new datasets or code. All data discussed are available in the published articles cited in the reference list.' No paper-specific phenotype datasets, images, code

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