Abstract
Smart agriculture is a new sector that integrates cutting-edge technologies for transforming conventional farming methods into sustainable farming methods, such as increasing crop yields, lower expenses, and conserving natural resources. Machine learning (ML) and deep learning (DL) are two significant techniques for smart agriculture that can be used to analyze enormous volumes of data and extract significant insights to enhance agricultural practices. In this context, ML and DL may be utilized for a number of tasks, including crop yield prediction, disease and pest detection, weather pattern monitoring, and irrigation and fertilization management. The proposed chapter investigates the utilization of ML and DL in smart agriculture and highlights some of the most promising uses of these technologies. The study addresses the obstacles and potential of adopting ML and DL in agriculture, such as data quality, privacy problems, and the requirement for specialized hardware and software. The study also looks at some of the most important developments in smart agriculture, including the usage of sensors, drones, and other IoT devices, as well as the integration of ML and DL with other technologies like precision farming and robotics. Overall, we believe that ML and DL have the ability to transform the way we produce food and manage our natural resources by empowering farmers to make better decisions, decrease waste, and boost production.