Emmanuel OLABAMERUN

This study examined the socio-economic effects of artificial intelligence in Agricultural by farmers and extension agents in Edo state, Nigeria. A stratified sampling procedure was used to select 131 farmers and 82 extension agents that were used for this study. The specific objectives were analyzed using descriptive statistics ( frequency, percentage and mean rating ) why regression analysis was used to analyze the hypothesis. Findings revealed that the majority of farmers (62.4%) and extension agents (61.7%) were male, and most fell within the economically active age range of 31–50 years. Awareness level of AI technologies was high, 74.4% and 91.7% of farmers and extension agents respectively, satellite imagery (64.0% of farmers; 75.0% of extension agents), and climate precision models (60.0% of farmers; 81.7% of extension agents). However, advanced technologies such as remote sensing recorded low adoption (8.0% of farmers; 1.7% of extension agents). Adoption levels of Artificial intelligence(AI) varied and showed mixed sociocultural reactions towards AI technologies. The regression analysis shows that most socio-economic characteristics, sex (β = 0.162; p = 0.020), association membership (β = 0.258; p < 0.01), awareness of AI (β = 0.585; p < 0.01), and location (p = 0.058) of respondents have significant influence on awareness ( β = 0.585; p < 0.01 ) and adoption (Adjusted R² = 0.523 ) of AI technologies with some sociocul tural effects. The study concludes that while awareness and partial adoption of AI technologies are increasing, full integration into agricultural practice is hindered by sociocultural beliefs, limited infrastructure, and gaps in technical capacity