Harnessing artificial intelligence for sustainable and data-driven agriculture

Abstract

Agriculture is undergoing a major transformation driven by artificial intelligence (AI), shifting traditional farming toward a more data-driven and technology-enabled system. This review explores the role of AI in modern agriculture, focusing on its key components, applications, benefits, limitations and future prospects. AI technologies such as machine learning, deep learning, computer vision, natural language processing, robotics, predictive analytics, IoT and cloud-edge computing are increasingly integrated into agricultural systems to enhance decision-making, productivity and sustainability. These technologies enable precision farming, crop monitoring, disease detection, forecasting, automation and supply chain optimization, improving efficiency while reducing resource wastage. However, AI adoption remains limited due to high costs, lack of technical skills, data and infrastructure gaps, regulatory uncertainties and concerns about privacy and adaptability. Additional challenges include farmer resistance and the potential loss of traditional agricultural knowledge, highlighting the need for a balanced integration of technology and human expertise. Looking ahead, advances in autonomous systems, multimodal data integration and adaptive learning are expected to make agriculture more resilient, inclusive and sustainable. Overall, AI offers strong potential to improve productivity and strengthen global food security under changing environmental conditions. Continued investment in research, digital infrastructure and capacity building will be essential to realize the full potential of AI for sustainable agricultural development.