Machine learning has evolved with big data technologies and high-performance computers to provide new opportunities for data intensive research in the multi-disciplinary agri-technology industry. In this article, we offer a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the supplied articles demonstrate how agriculture will benefit from machine learning technologies. By integrating machine learning to sensor data, farm management systems are evolving into real time artificial intelligence driven apps that provide rich recommendations and insights for farmer decision support and action.