Applied Chemical Engineering

AI-based monitoring and management in smart aquaculture for ocean fish farming systems

Pramod Dhamdhere

Department of Computer Engineering, Marathwada Mitramandal’s Institute of Technology, Lohgaon, Pune,411047, Maharashtra, India

Swati Mukesh Dixit

Department of Electronics and Telecommunication Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri, 411018, Pune, India; School of Technology and Research, Dr. D. Y. Patil Dnyan Prasad University, Sant Tukaram Nagar, Pimpri, Pune, 411018, Maharashtra, India.

Manjusha Tatiya

Department of Artificial Intelligence and Data Science, Indira College of Engineering and Management, Indira Chanakya Campus (ICC), Parandwadi, Pune - 410506, Maharashtra ,India

Babaso A. Shinde

Department of Artificial Intelligence and Data Science, Marathwada Mitramandal’s Institute of Technology, Lohgaon, Pune - 411047, Maharashtra, India

Jyoti Deone

Department of Information Technology, D.Y. Patil Deemed to be University RAIT, Navi Mumbai, Maharashtra 400706, India

Anant Kaulage

Department of Computer Engineering, MIT Art, Design and Technology University, Loni Kalbhor, Pune,412201, Maharashtra ,India

Yogendra Patil

Department of Computer Engineering, Marathwada Mitramandal’s Institute of Technology, Lohgaon, Pune,411047, Maharashtra, India

Rupesh Gangadhar Mahajan

Department of Computer Engineering, Dr. D. Y. Patil Institute of Technology, Sant Tukaram Nagar, Pimpri, 411018, Pune, Maharashtra, India; School of Technology and Research, Dr. D. Y. Patil Dnyan Prasad University, Sant Tukaram Nagar, Pimpri, Pune, 411018, Maharashtra, India.

Anant Sidhappa Kurhade

Department of Mechanical Engineering, Dr. D. Y. Patil Institute of Technology, Sant Tukaram Nagar, Pimpri, 411018, Pune, Maharashtra, India; School of Technology and Research, Dr. D. Y. Patil Dnyan Prasad University, Sant Tukaram Nagar, Pimpri, Pune, 411018, Maharashtra, India.

Shital Yashwant Waware

Department of Mechanical Engineering, Dr. D. Y. Patil Institute of Technology, Sant Tukaram Nagar, Pimpri, 411018, Pune, Maharashtra, India. 9 School of Technology and Research, Dr. D. Y. Patil Dnyan Prasad University, Sant Tukaram Nagar, Pimpri, Pune, 411018, Maharashtra, India.

DOI: https://doi.org/10.59429/ace.v8i3.5746

Keywords: Artificial intelligence, biomass estimation, disease detection, feed optimization, smart aquaculture, water quality monitoring

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Abstract

Background: The growing global demand for seafood and the limitations of conventional aquaculture practices have highlighted the need for sustainable and efficient alternatives. Ocean-based fish farming faces challenges such as inconsistent water quality, delayed disease detection, and inefficient feeding strategies. Artificial Intelligence (AI), integrated with the Internet of Things (IoT), computer vision, and machine learning, offers opportunities to address these issues and advance smart aquaculture systems. Methods: This review systematically synthesizes literature, industrial reports, and case studies from leading aquaculture regions including Norway, Japan, India, and Chile. The analysis focuses on AI applications in water quality monitoring, fish health management, feeding optimization, biomass estimation, and decision support. The study also evaluates commercial platforms and identifies technical, economic, and ethical challenges, alongside emerging research directions. Results: AI-based monitoring and management systems demonstrated significant improvements in aquaculture practices. Commercial solutions such as eFishery, Aquabyte, and Aquaai reported feed cost reductions of 15–30%, early disease detection leading to up to 20% lower mortality rates, and more accurate biomass estimation exceeding 90% prediction accuracy. These outcomes resulted in enhanced yield, cost savings, operational efficiency, and compliance with environmental standards. Conclusion: AI technologies have shown transformative potential in achieving sustainable, climate-resilient aquaculture. While challenges such as data scarcity, high setup costs, environmental variability, and ethical concerns persist, emerging approaches—including multimodal AI, digital twins, robotics, and explainable AI—can enhance robustness and transparency. Future research should emphasize scalable, adaptive, and standardized AI frameworks to support global seafood security and long-term sustainability in ocean-based fish farming.

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