Artificial intelligence in agronomy: A new era of crop management

Abstract

The integration of Artificial Intelligence (AI) into agronomy marks a transformative shift in crop management, offering enhanced precision, predictive analytics, and decision-making capabilities. This review explores the multifaceted role of AI technologies such as machine learning (ML), deep learning (DL), computer vision, and robotics in improving agronomic practices. We examine AI's applications in soil health monitoring, crop phenotyping, pest and disease detection, yield prediction, irrigation management, and climate resilience. The review also discusses challenges, future directions, and the socio-economic implications of AI adoption in agriculture. This synthesis aims to provide a comprehensive understanding for researchers, practitioners, and policymakers on leveraging AI for sustainable and efficient crop production.