Intelligent Aquaculture System for Fish Disease Detection Using Machine Learning

Year : 2025 | Volume : 12 | Issue : 02 | Page : 30 37
    By

    K. Sheela Sobana Rani1,

  • Harish M.,

  • Lokesh Kumar V.,

  • Vishnu V.,

  • Thanush Athithya S.,

  1. Student, Department of Electronics and Telecommunications Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  2. Student, Department of Electronics and Telecommunications Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  3. Student, Department of Electronics and Telecommunications Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  4. Student, Department of Electronics and Telecommunications Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  5. Student, Department of Electronics and Telecommunications Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India

Abstract

Aquaculture is one of the key factors for global food security, but fish diseases bring about heavy economic losses and jeopardize sustainability. One of the most important aspects of global food security is aquaculture, but fish infections endanger sustainability and cause significant financial losses. Early diagnosis is not possible since traditional disease detection techniques are laborious and necessitate expert intervention. To effectively detect fish infections, this study suggests an Intelligent Aquaculture System that uses machine learning. The system encompasses water quality monitoring, image-based disease recognition, and predictive analytics to identify infections at an early stage. To detect illnesses early, the system uses predictive analytics, image-based disease detection, and water quality monitoring. To classify diseases with high accuracy, machine learning models are trained using a collection of photos of healthy and diseased fish as well as environmental characteristics. Proactive steps to stop outbreaks are made possible by real-time data from sensors and cameras, which improve detection accuracy. The suggested methodology promotes sustainable aquaculture methods, lowers losses, and enhances disease management. ML models are trained using a dataset of diseased and healthy fish images, along with environmental parameters to classify diseases with high accuracy. Real- time data from sensors and cameras enhance detection precision, allowing for proactive measures to prevent outbreaks. The proposed system improves disease management, reduces losses, and supports sustainable aquaculture practices.

Keywords: LoRa, WSN, IoT, low power consumption, open source

[This article belongs to Journal of Telecommunication, Switching Systems and Networks ]

How to cite this article:
K. Sheela Sobana Rani1, Harish M., Lokesh Kumar V., Vishnu V., Thanush Athithya S.. Intelligent Aquaculture System for Fish Disease Detection Using Machine Learning. Journal of Telecommunication, Switching Systems and Networks. 2025; 12(02):30-37.
How to cite this URL:
K. Sheela Sobana Rani1, Harish M., Lokesh Kumar V., Vishnu V., Thanush Athithya S.. Intelligent Aquaculture System for Fish Disease Detection Using Machine Learning. Journal of Telecommunication, Switching Systems and Networks. 2025; 12(02):30-37. Available from: https://journals.stmjournals.com/jotssn/article=2025/view=215015


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Regular Issue Subscription Original Research
Volume 12
Issue 02
Received 05/04/2025
Accepted 01/05/2025
Published 24/05/2025
Publication Time 49 Days
170

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