Artificial intelligence and machine learning approaches for smart agriculture

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in agriculture has transformed traditional farming into a more efficient, data-driven, and sustainable practice. Smart agriculture leverages AI-driven techniques such as predictive analytics, image processing, and Internet of Things (IoT) sensors to optimize crop monitoring, irrigation management, pest detection, and yield prediction. Machine learning models enhance decision-making by analyzing vast datasets related to soil health, weather conditions, and plant diseases. This review explores various AI and ML approaches in smart agriculture, highlighting their applications, benefits, and challenges. It also discusses advancements in deep learning, computer vision, and automation technologies that are shaping the future of precision farming. Despite the significant progress, issues related to data availability, model accuracy, and implementation costs remain barriers to widespread adoption. The study concludes with future research directions and the potential of AI-driven smart agriculture to enhance global food security and sustainability