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Related Experiment Video

Updated: Feb 17, 2026

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

Graphene In-Sensor Compute Device for Plant Hydration Monitoring.

Utkarsh Misra^1,2, Philip Varkey^1,2, Ning Liu¹

¹Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States.

|February 16, 2026

Summary

This summary is machine-generated.

Graphene-based sensors mimic brain synapses for plant hydration monitoring. These ultralow-power, leaf-integrated devices enable real-time water content tracking without harming plants.

Keywords:

biosensor graphene in-sensor compute leaf hydration sensor neuromorphic computing

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Area of Science:

Materials Science
Biotechnology
Neuromorphic Engineering

Background:

Analog sensing devices with artificial synaptic behavior are key for scalable, energy-efficient environmental monitoring.
Graphene-based materials offer unique electronic properties for advanced sensor applications.

Purpose of the Study:

To develop and evaluate graphene-based, leaf-gated in-sensor compute devices for plant hydration monitoring.
To demonstrate the devices' capability for both sensing hydration and synapse-like conductance modulation.

Main Methods:

Fabrication of graphene-based, leaf-gated transistors for in-sensor computation.
Utilizing channel conductance trends to measure plant water content and ion mobility.
Encoding memory-like states through electrical stimulation for neuromorphic applications.

Main Results:

Devices exhibited synapse-like conductance modulation, tracking hydration-dependent ion mobility in leaves.
Demonstrated linear potentiation, depression, and short-term memory retention.
Achieved ultralow power operation (23 aJ/μS write energy, 0.23 μW read power) and minimal weight (9 mg) on Monstera leaves without physiological disruption.

Conclusions:

Graphene-based in-sensor compute devices provide a promising platform for real-time, long-term plant hydration monitoring.
Integration of computation and sensing in a biocompatible platform minimizes data transmission and enables edge neuromorphic applications.
The technology offers a scalable and energy-efficient solution for precision agriculture and environmental sensing.