A fuzzy logic-driven system for precise water quality monitoring and management in enhancing aquatic farming

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Published: 2025-06-26
DOI: https://doi.org/10.70102/ph6t5z22
Keywords:
Internet of things, Aquatic farming, Fuzzy logic, Water quality, Monitoring, dissolved oxygen

Main Article Content

Umida Avezova
Teacher, Department of Ecology and Life Safety, Urgench State University, Uzbekistan.
Hasssan Muhamed Ale
Department of Computers Techniques Engineering, College of Technical Engineering, Islamic University in Najaf, Najaf, Iraq; Department of Computers Techniques Engineering, College of Technical Engineering, Islamic University in Najaf of Al Diwaniyah, Al Diwaniyah, Iraq.
Sanat Chuponov
Senior Lecturer, Mamun University, Khiva, Uzbekistan.
Krishnan Ramesh
Department of Marine Engineering, AMET University, Kanathur, Tamil Nadu, India.
Fayzulla Khaitov
Associate Professor, Jizzakh State Pedagogical University (JDPU), Uzbekistan.
Dr. Rahman F
Assistant Professor, Department of CS & IT, Kalinga University, Raipur, India.

Abstract

Constraints on natural resources and global warming have made premium seafood a global issue in modern culture. By implementing fish farming IoT technologies, seafood production can increase significantly by optimizing resource consumption and improving fish growth rates. Such objectives require control, measurement, and monitoring of parameters like temperature, pH, water level, and feeding rate, along with fish growth structure. Proper fish farming is crucial to global food production; hence, suitable water parameters are fundamental for the development and wellbeing of aquatic organisms. This work offers a flexible and efficient method by using a precise water quality monitoring and maintenance system (PWQM&M) for AF pools. This work is distinguished by applying fuzzy logic to AF systems, aiming to improve management and boost accuracy while overcoming the rigidity of conventional fuzzy-based control systems. By doing so, superior performance is achieved in terms of autonomy, responsiveness, and speed while facilitating dynamic Water Quality (WQ) management with no human input. WQ monitoring adds value by increasing system performance; controlling AF systems with such complexity enables local WQ management manipulation across the diverse parameters of AF environments. An operational test conducted over 48 hours, during which appropriate levels of oxygen and salt particles were maintained, demonstrates the efficacy of the system for its intended purpose. This further demonstrates the system's effectiveness and performance in real-world scenarios.

Article Details

Issue

Volume 5, Issue S1 (June), 2025

Section

Articles

How to Cite

A fuzzy logic-driven system for precise water quality monitoring and management in enhancing aquatic farming (U. Avezova, H. Muhamed Ale, S. Chuponov, K. Ramesh, F. Khaitov, & R. F, Trans.). (2025). International Journal of Aquatic Research and Environmental Studies, 5(S1), 30-38. https://doi.org/10.70102/ph6t5z22

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