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An Integrated Smart Pond Water Quality Monitoring and Fish Farming Recommendation Aquabot System.

Md Moniruzzaman Hemal1, Atiqur Rahman1, Nurjahan1,2

  • 1Department of IoT and Robotics Engineering, Bangabandhu Sheikh Mujibur Rahman Digital University, Bangladesh, Kaliakair, Gazipur 1750, Bangladesh.

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Summary

AquaBot, an Internet of Things (IoT) system, uses robotics and machine learning (ML) to monitor water quality and recommend fish species. This technology enhances fish farm productivity and profitability by reducing costs and manual labor.

Keywords:
Internet of Things (IoT)machine learningreal-time water qualitysmart fish farmingsmart robotsolar power

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Area of Science:

  • Aquaculture technology
  • Artificial Intelligence in Agriculture
  • Environmental Monitoring Systems

Background:

  • Traditional fish farming faces economic losses due to labor-intensive monitoring, disease outbreaks, and fish mortality.
  • Automated fish species recommendation systems are needed to optimize profitability based on water quality.
  • Effective water quality monitoring can reduce operational costs and increase fish yields.

Purpose of the Study:

  • To develop an IoT-based system (AquaBot) for automatic water quality assessment and fish species recommendation.
  • To design a mobile robot for collecting water parameters like pH, temperature, and turbidity.
  • To evaluate various machine learning algorithms for accurate fish species recommendation.

Main Methods:

  • An IoT system with a mobile robot was developed to collect real-time water quality data.
  • Web and mobile interfaces were created for data monitoring and visualization.
  • Multiple machine learning algorithms, including an ensemble model, were trained and tested on a preprocessed dataset.
  • Performance evaluation used metrics such as accuracy, precision, recall, F1-score, MCC, and AUC.

Main Results:

  • The proposed custom ensemble model achieved 94% accuracy, precision, and recall, with a 94% F1-score.
  • The ensemble model also demonstrated strong performance with a 93% MCC and the best AUC score for multi-class classification.
  • The best-performing ML model was deployed on a web interface for practical application.

Conclusions:

  • AquaBot effectively monitors water quality and recommends suitable fish species, enhancing aquaculture operations.
  • The system is projected to increase production, reduce costs, and decrease manual labor in fish farming.
  • This integrated approach of IoT, robotics, and ML offers a significant advancement for modern aquaculture.