Evaluating the Efficacy of Generative AI in Agricultural Extension Services for Smallholder Farmers: A Systematic Literature Review

The integration of generative artificial intelligence (AI) into agricultural extension services presents a transforma- tive opportunity to address the unique challenges faced by smallholder farmers, particularly in resource-constrainedsettings. While traditional extension services often struggle with scalability and personalized support, generative AI offers potential solutions through dynamic content generation, real-time decision-making assistance, and adaptive learning tools. This systematic literature review examines the efficacy of generative AI in enhancing agricultural extension services, focusing on its applications, benefits, and limitations for smallholder farmers. We synthesize existing research across multiple dimensions, including AI-driven farmer support, IoT-enabled monitoring, andclimate-smart agriculture, to identify gaps and trends in the current knowledge landscape. A rigorous methodol- ogy was employed to select and analyze relevant studies, ensuring a comprehensive evaluation of both theoreticalframeworks and practical implementations. The findings reveal that generative AI can significantly improve access to tailored agricultural advice, optimize resource allocation, and mitigate climate-related risks; however, challengessuch as digital literacy, infrastructure limitations, and ethical concerns remain critical barriers to widespread adop- tion. The review also highlights the disproportionate focus on high-income regions, underscoring the need for moreinclusive research in low-resource agricultural systems. By consolidating these insights, we provide actionable rec- ommendations for policymakers, researchers, and practitioners to harness generative AI’s potential while addressingits socio-technical constraints, thereby fostering equitable and sustainable agricultural development.