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Smart Energy Management in Agricultural Wireless Sensor Nodes Using TinyML-Based Adaptive Sampling.

Adrian Hinostroza1, Jimmy Tarrillo1, Moises Nuñez1

  • 1Department of Electrical and Mechatronics Engineering, Universidad de Ingeniería y Tecnologia-UTEC, Lima 15063, Peru.

Sensors (Basel, Switzerland)
|April 14, 2026
PubMed
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This study introduces an energy-efficient smart sensor system for agriculture using adaptive sampling and data batching. The smart energy management system significantly reduces power consumption in remote pitaya plantations.

Area of Science:

  • Agricultural Technology
  • Sensor Networks
  • Energy Harvesting

Background:

  • Smart sensors are vital for data-driven agriculture but face power limitations in remote locations.
  • Pitaya plantations, like other remote farms, struggle with limited electricity access and maintenance for sensor networks.
  • Optimizing energy consumption is critical for the sustainability and scalability of agricultural IoT (Internet of Things) deployments.

Purpose of the Study:

  • To develop a smart energy management system for agricultural sensor nodes.
  • To reduce power consumption in remote agricultural settings without compromising data accuracy.
  • To enhance the operational lifespan of sensor nodes in resource-constrained environments.

Main Methods:

  • Implemented a machine learning model (Stochastic Gradient Descent regressor) for on-device adaptive sampling interval prediction.
Keywords:
TinyMLadaptive samplingenergy efficiencysmart agriculturewireless sensor network

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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  • Integrated a batching strategy to adjust data transmission frequency based on battery State of Charge (SOC).
  • Utilized Long Range (LoRa) for data transmission, optimizing packet size and frequency.

    • Main Results:

    • Achieved a 77.8% reduction in energy consumption compared to traditional fixed-interval sampling methods.
    • Maintained high temperature fidelity with a Mean Absolute Error (MAE) of 0.537 °C for temperature reconstruction.
    • Demonstrated the system's effectiveness through field experiments in a pitaya plantation setting.

    Conclusions:

    • The proposed smart energy management system significantly enhances the energy efficiency of agricultural sensor nodes.
    • Adaptive sampling and intelligent data batching are effective strategies for overcoming power constraints in remote IoT applications.
    • This approach enables more sustainable and cost-effective deployment of sensor networks in precision agriculture.