AI Applications in Aquaculture and Fisheries Production in Nigeria: Potentials and Challenges

Abstract

The need to increase fish supply for consumption is driving the aquaculture sector to seek for practices that will improve and increase production. In recent time, modern technology such as the use of Artificial Intelligence (AI) is being considered. Artificial Intelligence (AI) is "the theory and development of computer systems that enables the performance of tasks that normally require human intelligence, such as visual perception, speech recognition, decision making, and translation between languages".  Application and programming for artificial intelligence (AI) enables the 
recognition of objects, languages, sounds, and images and decision-making without requiring human oversight. Artificial Intelligence (AI) is an all-encompassing tool that is now being used to revolutionizing various fields by allowing individuals to reconsider how we integrate information, analyze data, and apply the ensuing insights for better decision-making. In some of developing countries, aquaculture is now evaluating and implementing AI as a means of increasing fish production through efficient and effective use of resources. This paper reviews the application of AI in enhancing the roles and operation of aquaculture and fisheries in Nigeria. Its emergence has led to significant advancements in automation, data analyses, and decision-making processes. This paper highlights the recent application of AI in developed countries and elaborates on the potential applications of AI in aquaculture and fisheries in Nigeria, most especially in areas like smart feeding systems, water quality management, fish disease control and prevention, market analysis, supply chain optimizations and aquaculture system management. It shows that integrating AI technologies in aquaculture and fisheries offers immense potential for improving production efficiency, reducing 
environmental impacts, and ensuring sustainable growth in the sector. However, the paper identifies several challenges and difficulties that must be addressed for its successful implementation in Nigeria. These include challenges such as limited data availability, cost and affordability, technical expertise and training, and adoption barriers, among others. The paper concludes by suggesting the way forward, such as comprehensive data collection and integration, adequate collaboration and research work, remote sensing and monitoring, capacity building and awareness, and sound policy 
framework and regulation.