AI-based Intelligent Aquaculture: Applications in Fish Detection, Smart Feeding, Biomass Estimation and Disease Management

JEETU; PRANAV KUMAR UPADHAYAY; VIRENDRA PRATAP; RINCHEN NOPU BHUTIA; ABHILASH THAPA

doi:10.54894/JISCAR.44.1.2026.181024

Authors

JEETU College of Fisheries, Dholi, Dr Rajendra Prasad Central Agricultural University, Pusa - 848 125, Bihar, India
PRANAV KUMAR UPADHAYAY College of Fisheries, Dholi, Dr Rajendra Prasad Central Agricultural University, Pusa - 848 125, Bihar, India
VIRENDRA PRATAP College of Fisheries, Dholi, Dr Rajendra Prasad Central Agricultural University, Pusa - 848 125, Bihar, India
RINCHEN NOPU BHUTIA ICAR-Central Soil Salinity Research Institute, Regional Research Station, Canning -743 329, West Bengal, India
ABHILASH THAPA College of Fisheries, Dholi, Dr Rajendra Prasad Central Agricultural University, Pusa - 848 125, Bihar, India

https://doi.org/10.54894/JISCAR.44.1.2026.181024

Keywords:

Artificial intelligence, Biomass estimation, Demand forecasting, Internet of things , Machine learning, Robotics

Abstract

Aquaculture has grown rapidly in the last 50 years; however, the industry is confronted by several significant operational challenges. These include disease outbreaks, ineffective feeding management, labour shortage, complexity of the supply chain, and environmental degradation. Artificial intelligence offers possibilities for potential to tackle these limitations. It plays an important role in enhancing operational efficiency by integrating advanced technologies such as machine learning, deep learning, computer vision, Internet of Things (IoT), big data analytics, cloud computing, and robotics in aquaculture system. This review brings together evidence from peer-reviewed research and relevant grey literature to provide a comprehensive overview of recent advances in AI for aquaculture, with a particular focus on fish detection, smart feeding, biomass estimation, disease management, and intelligent farm management. An AI-powered system enables more real-time monitoring of fish behaviour, automatic distribution of feed, accurate biomass estimation, clarification of species and early detection of pathogenic threats. Advanced deep learning models (e.g., convolutional neural network (CNN) and YOLO based architectures) have shown high accuracy in fish identification and behaviour analysis. Moreover, sensor-based monitoring of key water quality parameters such as temperature, pH, oxygen, ammonia, nitrite, and salinity optimizes environmental control, enhances fish survival rate and feed conversion efficiency. AI goes beyond the farm level and improves supply chain management through predictive demand forecasting, inventory optimization and advanced traceability. The AI based intelligent aquaculture systems provide significant improvements to productivity, environmental sustainability and functioning efficiency even with challenges such as high implementation costs and technical complexities. As a result, these systems play a key role in enhancing global food security and responsible aquaculture practices.

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