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Intelligent IoT-enabled data analytics and monitoring framework for smart freshwater recirculating aquaculture systems

Inomjon Matkarimov-Associate Professor, Department of Economy , Mamun University , UzbekistanRamee Riad Hwsein- Department of Computers Techniques Engineering , College of Technical Engineering , Islamic University in Najaf , Najaf , Iraq;Maqsad Matyakubov-Teacher, Department of Fruits and Vegetables , Urgench State University , UzbekistanBhoopathy BhaskaranDilafruz EshbekovaPriya VijDepartment of Computers Techniques Engineering , College of Technical Engineering , Islamic University in Najaf of Al Diwaniyah , Al Diwaniyah , Iraq
2025en
ABI

Annotatsiya

The global population is 8.1 billion and continues to expand, resulting in a greater need for food. Fish is an abundant resource for minerals, antioxidants, proteins, and micronutrients. It is a crucial component of nutrition for customers, particularly in impoverished and developing nations. It is a significant difficulty for farmers to meet consumer demand for nutritious seafood. A recirculating aquaculture system (RAS) serves as a mechanism to bridge the disparity between seafood production and consumption. The use of regulated conditions for RAS production has significantly risen; nonetheless, substantial losses occur due to laborious technology and managerial failures. Agricultural producers need timely and precise data to oversee and optimize output capacity. This study presents a smart freshwater recirculating aquaculture system (SF-RAS) using an IoT-enabled data analytics and monitoring framework (IoT-DAMF). The suggested system incorporates sensors and controllers. The network of sensors oversees aquatic variables, while controllers regulate the RAS ecosystem. An advanced DAMF significantly contributed to the oversight and preservation of the SF-RAS. The analysis established the correlation among the aquatic characteristics and revealed the corresponding variation. The empirical assessment indicates that the proposed method exhibits the best correlation for tracking relative changes in RAS characteristics.

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