Open Access

ARTICLE

Enhanced Water Quality Control Based on Predictive Optimization for Smart Fish Farming

Azimbek Khudoyberdiev¹, Mohammed Abdul Jaleel¹, Israr Ullah², DoHyeun Kim^3,*

1 Department of Applied Informatics, Kimyo International University in Tashkent, Tashkent, 100121, Uzbekistan
2 Department of Computer Science, Virtual University of Pakistan, Lahore, 54000, Pakistan
3 Department of Computer Engineering, Jeju National University, Jeju, 63243, Korea

* Corresponding Author: DoHyeun Kim. Email:

Computers, Materials & Continua 2023, 75(3), 5471-5499. https://doi.org/10.32604/cmc.2023.036898

Received 15 October 2022; Accepted 23 December 2022; Issue published 29 April 2023

Abstract

The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations. Internet of Things (IoT) based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish production. This objective requires intensive monitoring, prediction, and control by optimizing leading factors that impact fish growth, including temperature, the potential of hydrogen (pH), water level, and feeding rate. This paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish farming. The proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption problems. Fish farm indoor and outdoor values are applied to predict the water quality parameters, whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted parameters. Fuzzy logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy consumption. To evaluate the efficiency of the proposed system, the overall approach has been deployed to the fish tank as a case study, and a number of experiments have been carried out. The results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30% of energy efficiency at the maximum actuator control rate compared with other control levels.

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Keywords

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