The global population, now at 8 billion and projected to reach 9.7 billion by 2050, necessitates a significant increase in food production. This escalating demand underscores the importance of artificial intelligence (AI) technologies in agriculture, which enhance resource optimization and productivity amid supply chain pressures and more frequent extreme weather events. A systematic literature review (SLR), conducted using the PRISMA methodology, examined AI applications in agriculture, encompassing 906 relevant studies from five electronic databases. From these, 176 studies were selected for bibliometric analysis, with a quality appraisal further refining the selection to 17 key studies. The review highlighted a notable rise in publications over the past five years, identifying over 20 AI techniques, including machine learning, convolutional neural networks, IoT, big data, robotics, and computer vision, as predominant. The research emphasized significant contributions from India, China, and the USA, focusing on sectors like crop management, prediction, and disease and pest management. The study concluded with an analysis of current challenges and future trends, pointing to promising directions for AI in agriculture to meet global food production demands.

Artificial intelligence, Agriculture, Systematic literature review

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