Artificial Intelligence with Climate-Smart Farming Practices to Enhance Food Security among Smallholder Farmers in Diverse Climatic Conditions and Farming Systems Typical Of Sub-Saharan Africa

Abstract

The aim of this research work was to empirically evaluate the Artificial intelligence (AI) with climate-smart farming practices to enhance food security among smallholder farmers in diverse climatic conditions and farming systems typical of Sub-Saharan Africa. Employing a mixed-methods approach, data were collected from 384 farmers across three agro-ecological zones between May and October 2024. Results revealed that AI-integrated climate-smart agriculture (AI-CSA) adoption significantly increased crop yields by 34.2% (p<0.001) and improved organic farming efficiency by 28.7%. Pest detection accuracy using AI-powered systems reached 92.3%, while soil health monitoring through machine learning algorithms achieved 89.1% precision. The study found that 67.4% of farmers reported enhanced food security outcomes, with income increases of 42.8% compared to conventional methods. Statistical analysis confirmed both hypotheses: AI integration significantly improves climate-smart agriculture effectiveness (χ²=45.67, p<0.001), and combined AI-CSA approaches enhance food security outcomes beyond traditional organic farming (F=78.23, p<0.001). These findings provide crucial insights for sustainable agricultural transformation in developing regions, offering evidence-based recommendations for policy makers and agricultural stakeholders.