During recent years, the combination of big data availability and increased computing power has propelled artificial intelligence (AI), particularly deep learning, to achieve remarkable success across various domains. These include computer vision, medical image processing, natural language processing, intelligent sensing, and automated decision-making. AI has demonstrated the ability to perform tasks that traditionally required human intelligence, often surpassing human capabilities in handling complex tasks involving large datasets.

The advancements in machine learning, coupled with the explosion of big data, present unique opportunities to address environmental and economic challenges in the exploitation and management of natural resources like fisheries. By integrating big data with machine learning, we can significantly improve resource management, enhance policy design, optimize resource allocation, boost productivity and profitability, and promote overall sustainability. For example, these technologies can be utilized to predict fish population trends, fish migration patterns, monitor bycatch through image and video analysis, efficiently allocate fishing efforts, assess the impact of climate change on fisheries, and make informed decisions in dynamic and data-rich environments.

In this context, we invite submissions for a special workshop focusing on leveraging machine learning and big data to tackle cutting-edge research challenges in fisheries resources. Topics of interest include, but are not limited to:

• Construction and synthesis of datasets for fisheries 

• AI/ML applications for optimizing fishing operations, such as predicting fish migration patterns, catch and fishing efforts and locations

• AI/ML solutions for improving timely observation and monitoring of catch and production

• AI/ML tools for supporting decision-making in fisheries 

• Development of AI/ML methodologies and technological systems

• Sustainability and environmental impact modelling with AI in seafood sector 

• AI for supply chain optimization in aquaculture

• Predictive analytics for fish growth and health

The workshop will combine keynote talks and presentations of the accepted papers. Papers should be in English. Regular papers should be 12 – 20 pages in length (excluding references) while short papers should be 6-11 pages in length (excluding references). 

All registered papers will be submitted for publishing by Springer and made available through SpringerLink Digital Library.