Detection and Monitoring of Volatile Organic Compounds (VOCs) via the Use of Engineered Chemical Sensors: An Underexplored Tool for Safeguarding Crop Health and Quality

Abstract

The composition and concentrations of volatile organic compounds (VOCs) released from plants change in response to biotic and abiotic conditions because their metabolic/physiological processes adjust to manage stress and ensure fitness, suggesting that VOC sensors can be harnessed to support rapid and noninvasive diagnosis. Advances in the development of VOC sensors that mimic biological olfactory systems have led to increased attempts to diagnose diseases in humans and plants, detect explosives and controlled substances, and monitor food quality by monitoring VOCs. However, compared with the widely deployed diagnostic tools based on pathogen-specific indicator proteins or nucleic acids, the deployment of VOC sensors for diagnosing plant health-related problems has been limited. Here, we review the materials, tools, and approaches employed to fabricate VOC sensor devices, including (i) analytical tools and schemes for the identification of informative VOCs for diagnosis, (ii) distinct types of VOC sensors and engineered materials used for chemical sensing, and (iii) signal processing tools for diagnostic determination. Rapid advancements in these areas will facilitate the development of sensitive, selective, and robust VOC sensor devices that can support chemical ecology research and help manage diverse problems related to plant and human health, environmental quality, processed food production, and security. Although the focus is on their application in recognizing and diagnosing diverse problems that affect crop health and quality, selected examples illustrating other applications are also noted to underscore their broad utility. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Code and data availability

This is a review article on VOC-based chemical sensors for crop health. The supplied blocks contain no public phenotype/trait datasets, plant images or sensor data, author analysis code, trained models, or supplements with such assets. All cited studies (e.g., Slavković et al. 2024, Cho et al. 2024) are prior work, and

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