This research investigates the potential of Artificial Intelligence (AI) to enhance agricultural productivity in Nigeria, addressing critical challenges such as climate variability, limited access to modern farming techniques, and the need for sustainable practices. Employing a mixed-methods approach, the study integrates qualitative and quantitative methodologies, including surveys, interviews with small-scale farmers, and data analysis of agricultural outputs. Key findings reveal that AI applications, such as predictive modeling for crop yields, Pest prevention, disease detection, and resource optimization, significantly improve farming efficiency and sustainability. The research highlights the importance of tailored AI solutions that consider local agro-ecological conditions and farmer capacities. Furthermore, it identifies barriers to AI adoption, including data availability and cultural variability, which may hinder the widespread implementation of these technologies. The implications of this study underscore the necessity for comprehensive training programs and supportive policy frameworks to facilitate the integration of AI in agriculture, ultimately contributing to food security and economic stability in Nigeria. This research not only provides actionable recommendations for stakeholders but also contributes to the broader discourse on sustainable agricultural practices in Nigeria.

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