Plant Wearable Sensors: A Comparative Review of Invasive and Non-Invasive Approaches for Real-Time Plant Health Monitoring

Abstract

Plant wearable sensors have emerged as a transformative technology for precision agriculture and plant phenotyping, enabling in situ, real-time, and continuous acquisition of physiological signals from plant surfaces or internal tissues. However, existing reviews have organized the literature by monitoring targets, sensing functions, or material platforms, without systematically comparing technologies from the fundamental dimension of the degree of intervention imposed on plants. Drawing on representative studies identified through a structured literature search, this review establishes a three-tier classification framework—invasive, minimally invasive, and non-invasive—and conducts a head-to-head comparison across six dimensions: signal characteristics, plant disturbance, long-term stability, manufacturing complexity, field deployability, and biosafety. The results reveal that invasive sensors (nanobionic probes, implantable microelectrodes, and organic electrochemical transistors) achieve nM–pM detection limits, yet wound responses generally limit their effective monitoring duration to the order of days; non-invasive sensors (flexible patches, strain sensors, and multimodal platforms) support weeks-to-months of continuous monitoring and are amenable to scaled deployment, but the indirectness of surface signals confines detection limits to the μM level; minimally invasive technologies (microneedle arrays and ultra-thin microelectrodes) offer a compromise between the two extremes. On this basis, a decision framework based on three-layer selection is proposed to guide technology selection across laboratory research, field deployment, and controlled environment agriculture. Future efforts should focus on standardized performance evaluation protocols, biodegradable self-powered systems, and the integration of invasive–non-invasive hybrid sensing networks with plant digital twins.

Code and data availability

This is a review article with no original phenotyping measurements or computational analysis. The Data Availability Statement explicitly states no new data were created or analyzed, and no public datasets, code, models, or paper-specific assets are mentioned.

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