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Related Experiment Video

Updated: Jan 11, 2026

Author Spotlight: Non-Invasive High-Resolution Measurement of Chlorophyll Synthesis During De-Etiolation
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Self-powered wearable plant chlorophyll sensing patch.

Xujun Chen1, Longgang Ma1, Jiawei Zhang1

  • 1College of Engineering, China Agricultural University, Beijing, 100083, PR China.

Biosensors & Bioelectronics
|November 16, 2025
PubMed
Summary

A novel self-powered system monitors plant chlorophyll non-destructively for smart agriculture. This system uses a plant chlorophyll sensing patch and a hybrid energy harvester, enabling early stress detection and yield prediction.

Keywords:
Chlorophyll sensingSMFCSelf-poweredSmart agricultureWTVNG

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Area of Science:

  • Agricultural Engineering
  • Sensor Technology
  • Renewable Energy

Background:

  • Accurate monitoring of plant chlorophyll is crucial for smart agriculture, enabling precise fertilization and yield prediction.
  • Existing chlorophyll measurement methods are often destructive, slow, or require external power, limiting their application in long-term, unmanned monitoring.
  • There is a need for a fast, non-destructive, and self-powered system for continuous chlorophyll monitoring.

Purpose of the Study:

  • To develop a fast, non-destructive, and long-term self-powered system for monitoring plant chlorophyll content.
  • To integrate a plant chlorophyll sensing patch (PCSP) with a hybrid energy harvesting system (PWSS) for sustainable operation.
  • To evaluate the system's performance in assessing chlorophyll content, detecting plant stress, and predicting crop yield.

Main Methods:

  • A miniaturized plant chlorophyll sensing patch (PCSP) was designed using a reflective optical mode with LEDs and photodetectors.
  • A triple energy system (PWSS) combining photovoltaics (PV), a wind energy harvesting nanogenerator (WTVNG), and a soil microbial fuel cell (SMFC) was developed for self-powering.
  • The chlorophyll content index (CCI) was calculated from PCSP data, and its correlation with actual chlorophyll content was analyzed.
  • The LSTM algorithm was employed to categorize stress and predict chlorophyll content based on collected data.

Main Results:

  • The PCSP demonstrated a strong linear relationship (r² > 0.9) between the calculated CCI and actual chlorophyll content.
  • The WTVNG achieved a short-circuit current density of 7.21 A/m² at 180 rpm, and the SMFC provided a power density of 1.18 mW/m³.
  • The integrated system successfully enabled long-term, unmanned monitoring and detected plant stress earlier than traditional methods.
  • The system showed promising applications for early stress detection and yield prediction in smart agriculture.

Conclusions:

  • The proposed self-powered chlorophyll monitoring system offers a viable solution for smart agriculture applications.
  • The hybrid energy harvesting system ensures continuous operation without external power sources.
  • Early detection of plant stress and accurate chlorophyll content estimation are achievable, paving the way for optimized crop management.