International Journal of Agriculture Extension and Social Development

The evolution of AI in agriculture has progressed from mechanization to data-driven decision-making, integrating big data, blockchain, and cloud computing for enhanced traceability and automation. However, challenges such as data availability issues, high costs, ethical concerns, and resistance from traditional farmers pose barriers to widespread AI adoption. Despite these limitations, AI has the potential to address food security challenges and climate resilience by optimizing agricultural operations and reducing environmental impact.

This paper explores AI-driven innovations, real-world applications in large-scale agribusinesses and SMEs, and policy-driven AI initiatives. It also highlights future directions, emphasizing the need for regulatory frameworks, farmer training programs, and AI-driven financial solutions to ensure sustainable and inclusive AI adoption in agriculture.