Machine Learning-Based Fish Species Recommendation Using Water Quality Parameters

Muhammad Owais Khan, Faheem Ul Haq, Aasif Awan
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Muhammad Owais Khan, Faheem Ul Haq, Aasif Awan: Robotics TeamHadaf Group of Colleges Peshawar, Pakistan. LecturerBiotechnologyHadafCollegeofAlliedHealth Sciences, Peshawar, Pakistan

International Journal of Innovations in Science & Technology, 2025, vol. 7, issue 7, 110-126

Abstract: The integration of machine learning (ML) in aquaculture enables data-driven fish species recommendations based on water quality parameters. Traditional fish farming faces challenges like manual monitoring, inefficient species selection, and unpredictable water conditions, leading to economic losses. This paper presents a software-based fish recommendation system using ML models to analyze seven key water parameters—pH, Temperature, Turbidity, TDS, Dissolved Oxygen, Nitrate, and Ammonia. Various ML algorithms, including Random Forest, XGBoost, and SVM, were evaluated, with the optimized model achieving over 90% accuracy. A graphical user interface (GUI) allows users to input parameters and receive real-time recommendations, enhancing efficiency and sustainability in aquaculture.

Keywords: Fish Farming; Machine Learning; Water Quality Analysis; XGBoost; Smart Aquaculture (search for similar items in EconPapers)
Date: 2025
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