Artificial Intelligence Adoption and Sustainable Agricultural Productivity: Implications for Food Security and Rural Development in Sub-Saharan Africa

Agriculture remains central to livelihoods, employment and food systems across Sub-Saharan Africa, yet the sector faces persistent constraints associated with low productivity, climate variability, land degradation, limited infrastructure, weak market access and uneven access to extension services. Artificial intelligence (AI) offers tools—including machine learning, computer vision, predictive analytics and natural language processing—that may strengthen agricultural decision-making through crop and disease monitoring, yield prediction, precision input management, irrigation scheduling, climate-risk analysis and digital advisory services. This article examines AI adoption and its implications for sustainable agricultural productivity, food security and rural development in Sub-Saharan Africa. It adopts an integrative literature review and conceptual-analysis approach, synthesising peer-reviewed studies and relevant institutional and policy literature on AI, digital agriculture, technology adoption and smallholder farming. The analysis is organised around the Technology Acceptance Model, Diffusion of Innovations, the Technology–Organisation–Environment framework and the Sustainable Livelihoods Approach. It argues that AI can contribute to productivity and sustainability when its outputs are locally relevant, affordable, accessible, trusted and integrated with agricultural extension, farmer organisations and market institutions. Technology availability alone, however, does not guarantee adoption or improved welfare. Constraints include limited rural connectivity and electricity, high acquisition and maintenance costs, inadequate digital skills, data scarcity, gendered access inequalities, weak institutional coordination and concerns about data ownership and algorithmic bias. The article develops an integrated conceptual framework and six propositions for future empirical testing. It concludes that AI should be treated as an enabling component of inclusive agricultural transformation rather than a substitute for land, infrastructure, extension, finance, local knowledge and effective public policy.