Artificial Intelligence and Deep Learning Innovations for Sustainable Aquaculture and Underwater Environments

Artificial intelligence (AI) and deep learning are emerging as transformative forces in the modern landscape of aquaculture and underwater environmental management. This rapidly evolving field has redefined best practices, equipping researchers and aquaculture practitioners with powerful tools for automating complex processes, enhancing the precision of biomass estimation, and enabling comprehensive monitoring of aquatic and marine environments. Recent breakthroughs demonstrate how state-of-the-art neural network architectures, such as convolutional neural networks (CNNs) and transformer-based models, are overcoming traditional constraints in underwater visual analysis ranging from turbidity and poor illumination to multi-species differentiation and challenging habitat complexity. The integration of AI with intelligent sensor networks and robotics is not only boosting operational efficiency but also advancing sustainability, animal welfare, and ecosystem resilience at a time when global seafood demand and environmental pressures are at an all-time high.Despite this momentum, the path toward fully integrated and scalable AI-driven systems in aquaculture and underwater research presents unique challenges. Key issues remain around generalizing algorithms to variable natural conditions, fusing multi-modal data streams from disparate sensors (e.g., optical, sonar, chemical, and IoT devices), and optimizing machine learning models for both accuracy and resource efficiency. The reliability of automated fish/prawn detection, the consistency of environmental monitoring under dynamic water quality, and the development of standardized benchmarks for evaluating AI performance are ongoing areas of investigation. There is also a vital need for frameworks that translate these innovations into commercial and regulatory contexts, ensuring that sustainability and socio-economic benefits are genuinely realized across diverse global aquaculture and ocean management scenarios.This Research Topic aims to gather cutting-edge research and case studies at the intersection of AI, deep learning, and sustainable aquatic resource management. Contributors are invited to present both methodological advances and practical deployments that address core questions, such as: How can advanced AI methods be robustly adapted for underwater environments? What new sensing architectures and learning frameworks have demonstrated real-world impact in aquaculture and marine ecology? How can data-driven insights facilitate sustainable growth while safeguarding environmental health?To further explore the reach and limits of these innovations, we welcome submissions addressing, but not limited to, the following themes:- Novel applications of AI and deep learning for aquaculture management- Automated biomass estimation using visual and multi-sensor data- Deep learning-enabled underwater object detection and species classification- Deployment and optimization of IoT-driven sensor networks in marine and aquaculture settings- Real-time, non-invasive monitoring of environmental and biological variables- Development and evaluation of robust and resource-efficient AI models for aquatic applications- Comparative studies of AI architectures in challenging underwater conditions- Benchmark datasets, validation frameworks, and performance metrics- Socio-economic impacts and sustainability assessments of AI innovations- Translational strategies for scaling AI and deep learning from pilot studies to broad adoption Topic editors: Pengcheng Liu, Ming Duan, Tiong Hoo Lim, Suriayati CHUPRAT Source: https://www.frontiersin.org/research-topics/84824/artificial-intelligence-and-deep-learning-innovations-for-sustainable-aquaculture-and-underwater-environments