Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming.

• This paper showcases the potential of information and communication technologies in traditional agriculture, as well as the issues to be encountered when they are applied to farming practices. • The challenges of robotics, IoT devices, and machine learning, as well as the roles of machine learning, artificial intelligence, and sensors used in agriculture, are all described in detail. In addition, drones are under consideration for conducting crop surveillance as well as for managing crop yield optimisation. • Additionally, whenever appropriate, global and state-of-the-art IoT-based farming systems and platforms are mentioned. We perform a detailed study of the recent literature in each field of our work. • From this extensive review, we conclude that the current and future trends of artificial intelligence (AI) and identify current and upcoming research challenges on AI in agriculture. The digitalization of data has resulted in a data tsunami in practically every industry of data-driven enterprise. Furthermore, man-to-machine (M2M) digital data handling has dramatically amplified the information wave. There has been a significant development in digital agriculture management applications, which has impacted information and communication technology (ICT) to deliver benefits for both farmers and consumers, as well as pushed technological solutions into rural settings. This paper highlights the potential of ICT technologies in traditional agriculture, as well as the challenges that may arise when they are used in farming techniques. Robotics, Internet of things (IoT) devices, and machine learning issues, as well as the functions of machine learning, artificial intelligence, and sensors in agriculture, are all detailed. In addition, drones are being considered for crop observation as well as crop yield optimization management. When applicable, worldwide and cutting-edge IoT-based farming systems and platforms are also highlighted. We do a thorough review of the most recent literature in each area of expertise. We conclude the present and future trends in artificial intelligence (AI) and highlight existing and emerging research problems in AI in agriculture due to this comprehensive assessment. [ABSTRACT FROM AUTHOR]