INTEGRATING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR SUSTAINABLE PRODUCTIVITY Arnab Chatterjee1, Sutapa Sany

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ISSN 2410-7751 (Print)
ISSN 2410-776X (Online)

cover biotech acta general

Biotechnologia Acta Т. 19, No. 1, 2026
P. , Bibliography 67, Engl.
UDC: 639.3:004.8:004.89:502/504
doi:

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INTEGRATING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR SUSTAINABLE PRODUCTIVITY

Arnab Chatterjee ¹, Sutapa Sanya ^{l, 2}

¹ University of Kalyani, Department of Zoology, Kalyani, Nadia, West Bengal, India
² Bidhannagar College, Department of Zoology, Bidhannagar, Kolkata West Bengal, India

Aim: The study aimed to evaluate the potential of Artificial Intelligence (AI) and Machine Learning (ML) in improving aquaculture production systems through enhanced monitoring, automation, and data-driven decision-making.

Methods: The study was conducted through a comprehensive analysis of recent experimental and field-based reports integrating AI-driven technologies in aquaculture. Models such as convolutional neural networks, recurrent neural networks, and AIoT-based digital twins were reviewed for their applications in monitoring fish growth, detecting disease, and controlling water quality. Various aquatic species, including tilapia, salmon, and carp, were referenced as model organisms in these studies to evaluate performance accuracy and operational efficiency.

Results: The findings revealed that AI-enabled image recognition models successfully detected fish health anomalies and feeding behaviours with high precision. Sensor-based water quality systems linked to AI algorithms improved environmental stability and reduced mortalities. Automated feeding and real-time decision-support frameworks minimized resource wastage, while predictive models optimized growth rates and harvesting schedules. Collectively, these advancements improved productivity and reduced operational costs while maintaining ecological balance.

Conclusion: Artificial Intelligence and Machine Learning have demonstrated transformative potential for advancing aquaculture toward greater sustainability, profitability, and environmental stewardship. Their integration supports intelligent farm management and resilience against climate and resource challenges.

Keywords: Aquaculture, Artificial Intelligence (AI), Machine Learning (ML), smart fish farming, sustainability.