Localization in Precision Agriculture: A Machine Learning Perspective

Due to population expansion, frequent climate change, and resource constraints, it is now challenging to supply the food needs of the current population. A cutting-edge method to address current difficulties with agricultural sustainability is known as precision agriculture. For effectively managing agricultural land it is a must to monitor it. By deploying the nodes, this task becomes easy. To facilitate effective information transmission, these nodes gather and transmit the data. Localizing the nodes becomes essential during data transfer. Aside from that, these networks are quite dynamic, and conventional localization methods are inadequate to address the problems brought on by quickly moving sensor nodes. As a result, machine learning (ML) approaches are used to solve these problems quickly and effectively. This paper provides a detailed discussion of precision agriculture (PA), the need for localization and ML algorithms in PA, and their limitations.