Comparative Analysis of Structural and Socioeconomic Factors Influencing AI-Based Agricultural Systems Across Developed and Developing Countries

Agriculture is increasingly using AI to boost yields, resource efficiency, and climate and environmental resilience. The use of AI in agriculture varies across developed and developing nations. Using a Systematic Literature Review (SLR), this research looked at how AI is used in agriculture, the structural and socioeconomic factors that influence adoption, and the tactics required to close the implementation gap. 318 documents were found and 54 were evaluated in the Scopus-based analysis. In developed environments, fully integrated, expensive, and automatable AI systems are typical, using robotic assistance, machine learning, and precision farming. However, affordable, modular, support-oriented monitoring, diagnostic, and advisory services are used in developing countries. There are differences in data management, funding, government assistance, infrastructure planning, and technical know-how. According to the paper, the implementation gap is caused by a lack of system preparedness, information input, and AI technology availability.