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An aquaculture automation system (AcAS) is a user-friendly single-window unit. This allows end users to monitor and control the entire system easily through a built-in, customizable graphical user interface. AcAS was designed for simplicity, making it easy to configure and use. This system was integrated with highly efficient industrial-grade envir...
Citations
... Integration of control systems, effective automation integrates various control systems, including feeding, water quality management and environmental control, to create a cohesive and responsive operation. Artificial intelligence (AI) decision-making, AI algorithms analyze the collected data to make informed decisions, optimizing resource allocation and farm management (Sasikumar et al., 2024). Resource optimization and automation seek to optimize resource utilization, reducing waste and ensuring the efficient use of energy, water and feed. ...
... • Training: Train aquaculture personnel to operate and troubleshoot the automation systems effectively. Knowledgeable staff are critical for maximizing the benefits of automation (Sasikumar et al., 2024). ...
... These autonomous robotic fish are designed to detect contaminants and monitor environmental health around aquaculture sites. Equipped with high-precision sensors, they navigate independently within the aquatic environment and communicate via low-frequency acoustic signals, enabling collective data gathering and coordinated responses to changes in water quality (Sasikumar et al., 2024). ...
The digital transformation of aquaculture is reshaping the sector by integrating advanced technologies that enhance efficiency and sustainability. This transformation is primarily driven by the adoption of Artificial Intelligence (AI), biological models and sophisticated data acquisition systems, which enable real-time monitoring and management of aquaculture operations. This chapter explores the applications of AI, robotics and biological models in various facets of aquaculture, highlighting their roles in enhancing productivity, sustainability and operational precision. AI-based solutions optimize feeding strategies, disease detection and water quality management by analyzing sensor data to ensure optimal conditions for fish growth and health, reducing manual intervention and minimizing environmental risks. Biological models simulate fish growth patterns, supporting informed decision-making, while IoT sensors and cloud computing enhance data acquisition, improving traceability and resource management. Robotics automate labour-intensive tasks, such as feeding and harvesting, with precision. Automated feeding systems, use robotic arms and computer vision, to precisely dispense feed based on fish feeding behavior, minimizing waste and ensuring optimal nutrition. Harvesting robots, guided by algorithms, sort and handle fish efficiently, reducing stress and enhancing yield quality. The integration of AI and robotics boosts operational efficiency, reduces labour costs and supports sustainable practices by optimizing resource use and minimizing environmental impacts. However, challenges like high initial investments, scalability and regulatory considerations remain significant. These technologies are advancing precision aquaculture, maximizing production outputs and environmental stewardship while reducing human intervention. Continued research and development are essential to address existing challenges and unlock the full potential of digital transformation in aquaculture.
