A Generative AI‐Assisted Piezo‐MEMS Ultrasound Device for Plant Dehydration Monitoring

Abstract

Abstract Plant health, closely tied to hydration, has a direct impact on agricultural productivity, making the monitoring of leaf water content essential. Current devices, however, are often invasive, bulky, slow, power‐inefficient, Complementary Metal‐Oxide‐Semiconductor (CMOS)‐incompatible, and unsuitable for large‐scale, re‐usable outdoor sensor networks. Utilizing micro‐electromechanical systems (MEMS) fabrication enables wafer‐scale miniaturization and precise control of ultrasound transducers, thereby enhancing sensitivity while significantly reducing power and cost. This work introduces the CMOS‐compatible, plant‐leaf attachable piezo‐MEMS ultrasound device (PMUT‐Leaf‐PMUT, PLP) for real‐time dynamic moisture monitoring and rapid one‐shot measurement of relative water content (RWC). Notably, the PLP is reattachable to pre‐calibrated plant leaves, enhancing reusability and reducing electronic waste. Employing piezoelectric micromachined ultrasound transducers (PMUTs) fabricated via piezoelectric over silicon‐on‐nothing (PSON), the device non‐invasively monitors hydration across diverse cultivars with a 70% relative water content (RWC) detection range. Generative deep learning using a conditional variational autoencoder (CVAE) translates electrical signals to precise hydration measurements, achieving an RWC root‐mean‐square error of 1.25%. The deployment of this generative AI‐assisted PLP system directly links plant responses to environmental shifts, representing a significant advancement in precision plant health management and irrigation practices, thereby substantially improving agricultural efficiency and promoting environmental conservation.

Keywords

• generative AI
• microelectromechanical Systems
• piezoelectricity
• plant health
• wearable