We previously reported about the automated monitoring of nets using artificial intelligence (AI), identifying the best delousers for salmon through AI-supported image analysis, and the USAID project to estimate aquaculture production and value chain performance using AI.
Most recently, numerous manuscripts on various applications of AI in aquaculture have been published. It includes the review of data mining and machine learning framework in aquaculture and fisheries, the development of smart aquaculture farm management system using IoT and AI-based surrogate models, Fish Disease Detection Using Image-Based Machine Learning Techniques in Aquaculture and many others.
And this is just the beginning. Already now, we are seeing the improvements that have been able to be achieved with the help of AI: the classification of abnormal appearances in cage-cultured fish, the development of more efficient feeding practises, and a system for early detection of AI-based systems for early detection of disease outbreaks.
AI technology has enormous potential for making modern aquaculture tasks less labour-intensive and improving animal welfare conditions. Though the costs of components required to implement AI systems for fish farming are relatively high, costs tend to lower as more companies use these technologies, and it is likely that eventually, it will become affordable (and therefore accessible) to small producers in countries with low GDP.
One key aspect that must be remembered, though, is the need for training and education to enable fish farmers to use the new technology appropriately. And here, the EIT Food-funded AGAPE project aims to make a difference by providing AI-driven recommendations for upskilling and reskilling to adapt the workforce’s skills to the aquaculture industry’s changing needs.