Artificial intelligence in small-scale fisheries

Abstract

Small-scale fisheries (SSF) support the livelihoods and food security of billions worldwide, yet they remain underrepresented in technological innovation efforts, including Artificial Intelligence (AI). Yet these fisheries face severe pressures stemming from compounding climate impacts, governance challenges, and persistent poverty conditions further exacerbated by inadequate data and limited visibility of their informal nature. While rapid advances in AI, including open-access Generative AI and Large Language Models, hold promise for improving management through complex modelling and innovative applications, the adoption of AI in SSF has lagged behind industrial fisheries and terrestrial agriculture. Using a comprehensive bibliometric and content analysis of 227 publications from the past decade, we evaluate the progress and scope of AI solutions in SSF and assess the constraints to broader uptake. Our findings reveal a lack of targeted research, limited resources, and scarce data, particularly in socio-economic aspects and studies utilising AI. This underscores the need for sustainable, context-sensitive AI tools. Building on these insights, we propose a taxonomy that aligns AI applications with Environmental, Social, Economic, and Governance (ESEG) sustainability dimensions, offering a roadmap for future exploration. This study highlights emergent research trends and provides a best practice framework and practical guidance to policymakers and researchers striving to leverage new digital and data advances to enhance fisheries and environmental sustainability.