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Related Concept Videos

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  11. Iot-enabled Effective Real-time Water Quality Monitoring Method For Aquaculture.
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Artificial Intelligence
  9. Intelligent Robotics
  11. Iot-enabled Effective Real-time Water Quality Monitoring Method For Aquaculture.

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IoT-enabled effective real-time water quality monitoring method for aquaculture.

Rupali P Shete1, Anupkumar M Bongale2, Deepak Dharrao3

  • 1Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune Campus, Lavale, Pune, Maharashtra, India.

Methodsx
|September 12, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study presents an IoT system for real-time aquaculture water quality monitoring. The technology helps prevent fish disease and mortality through accurate sensor data, reducing human intervention.

Keywords:
AquacultureDissolved oxygen sensorFish healthIoT

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Area of Science:

  • Aquaculture
  • Environmental Monitoring
  • Internet of Things (IoT)

Background:

  • Aquaculture is a growing industry crucial for sustainable food and economic development.
  • Effective water quality monitoring is essential for high-yield aquafarming.
  • Technology integration, specifically IoT, offers solutions for real-time monitoring and control.

Purpose of the Study:

  • To introduce a comprehensive method for integrating IoT sensors into aquafarming environments.
  • To enable accurate, real-time measurement of critical water quality parameters (temperature, pH, Dissolved Oxygen).
  • To reduce fish disease and mortality rates with minimal human intervention and cost.

Main Methods:

  • Utilized Arduino boards and communication modules for IoT sensor integration.
IoT-Based Water Quality Monitoring and Data Analysis in Aquaculture
Water quality monitoring
  • Designed and developed a compact Printed Circuit Board (PCB) for accurate sensor readings and reliable communication.
  • Performed instrument calibration and cross-validated automated data with manual observations.

    • Main Results:

    • Achieved accurate real-time data collection for temperature, pH, and Dissolved Oxygen (DO).
    • Demonstrated the system's capability to prevent fish disease and mortality through data-driven decisions.
    • Ensured precise sensor measurements through rigorous calibration and validation tests.

    Conclusions:

    • The developed IoT system provides an effective solution for intelligent aquafarming.
    • Real-time water quality monitoring significantly improves aquaculture sustainability and economic viability.
    • Data-driven insights from IoT sensors are crucial for proactive fish health management.