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MotionLeaf: Fine-grained Multi-leaf Damped Vibration Monitoring for Plant Water Stress Using Cost-effective mmWave Sensors

Mark Cardamis,Chung Tung Chou,Wen Hu

Published 2025 in Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

ABSTRACT

Water stress significantly impacts plant health and crop yields worldwide. Traditional methods, such as soil moisture sensors, often lack accuracy, are invasive, and labor-intensive. This paper introduces MotionLeaf, a novel mmWave-based prototype system that assesses plant stress by measuring vibration frequencies across multiple leaves. MotionLeaf features a specialized signal processing pipeline to estimate fine-grained damped frequencies from noisy micro-displacement measurements captured via mmWave radar. Specifically, the Interquartile Mean (IQM) of phase differences from neighboring Frequency-Modulated Continuous Wave (FMCW) radar chirps is used to calculate micro-displacements. Additionally, multiple radar antennas isolate the vibration signals of individual leaves through a Blind Source Separation (BSS) method. Experimental results show that MotionLeaf measures leaf vibration frequencies with an average error of 0.0176 Hz, less than half of the 0.0416 Hz error of the state-of-the-art approach (mmVib [26]). In practical drought experiments, MotionLeaf effectively indicated water stress through observed day-night frequency variations below 0.06 Hz over a seven-day trial. Furthermore, additional validation using fan-generated wind confirmed the feasibility of passive excitation in outdoor environments, achieving low-frequency measurement errors (approximately 0.03 Hz) at wind speeds above 2.5 m/s. These results underscore the effectiveness and potential of MotionLeaf as a scalable, non-invasive solution for accurately detecting plant water stress in realistic agricultural scenarios.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

  • Publication date

    2025-09-03

  • Fields of study

    Agricultural and Food Sciences, Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.1145/3749547
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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