Artificial Intelligence for Sustainable and Welfare-Centered Aquaculture: From Intelligent Monitoring to Optimized Production

Aquaculture is increasingly moving towards data-driven and precision-based production systems in response to the growing demand for aquatic food, the need for efficient resource utilization, and increasing emphasis on animal health, welfare, and environmental sustainability. Recent advances in artificial intelligence (AI), machine learning, deep learning, computer vision, sensor technologies, and advanced statistical approaches offer significant opportunities to transform aquaculture production and management.This Research Topic aims to bring together interdisciplinary research on the application of AI and intelligent technologies to develop productive, sustainable, and welfare-centred aquaculture systems. Particular emphasis will be placed on applications that translate biological and environmental data into actionable information for improving animal welfare, production efficiency, resource management, and environmental performance.We welcome contributions addressing AI-assisted monitoring and assessment of fish and shellfish behaviour, welfare, health, stress, feeding activity, growth, biomass, and mortality. Research on computer vision and deep learning for non-invasive welfare assessment, behavioural phenotyping, disease and stress detection, and automated production monitoring is particularly encouraged.This Research Topic will also cover AI-driven approaches for optimizing feeding, stocking density, water quality, energy and water use, production conditions, and other critical management variables. Contributions integrating AI with design of experiments, statistical modelling, response surface methodology, bayesian optimization, machine learning-guided experimentation, and multi-objective optimization are encouraged to demonstrate how intelligent approaches can improve experimental efficiency and identify optimal production conditions.We also welcome studies on precision aquaculture, digital twins, intelligent decision-support systems, explainable and trustworthy AI, multimodal data integration, real-time monitoring, and AI deployment in commercial and research aquaculture systems.Importantly, the Research Topic seeks to move beyond proof-of-concept applications by highlighting studies involving biological validation, field or farm-scale evaluation, interpretability, reproducibility, and measurable improvements in animal welfare, production efficiency, resource use, and environmental sustainability.Key Themes• AI and deep learning for aquaculture• Computer vision for fish and shellfish welfare assessment• Automated behavioural and health monitoring• AI-assisted disease and stress detection• Intelligent feeding and growth optimization• Biomass estimation and production forecasting• AI-driven water-quality and environmental management• Resource and energy optimization• AI-assisted Design of Experiments and experimental optimization• Precision and smart aquaculture• Digital twins and intelligent decision-support systems• Explainable, interpretable, and responsible AI• Integration of AI, sensor, behavioural, production, and environmental data• AI applications for sustainable and welfare-centred aquaculture Topic editors: Eldho Varghese, Suresh V. V. R. Source: https://www.frontiersin.org/research-topics/85537/artificial-intelligence-for-sustainable-and-welfare-centered-aquaculture-from-intelligent-monitoring-to-optimized-production