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ABSTRACT It has been observed that the agriculture sector has picked exponential growth with the help of the integration of advanced technology like wireless sensor networks (WSNs), the Internet of Things (IoT), and machine learning (ML). They have not only created new opportunities but also boosted the efficiency and productivity in the sector. Out of so many significant hurdles, accurately pinpointing the positions of the sensor nodes and agricultural assets like crops, livestock, and machinery within the field is one major challenge. Merging ML with a WSN‐assisted IoT (WIoT) network has offered a promising solution to overcome the localization challenge for smart agriculture. This integration will greatly benefit agricultural practices, such as precision agriculture, fast decision‐making, accurate positioning of sensor nodes, monitoring, and managing real‐time data effectively. This paper comprehensively discusses state‐of‐the‐art techniques and methodologies employed in localization within WIoT networks for smart agriculture. It explores various ML algorithms, which include reinforcement learning, supervised learning, and unsupervised learning, to perform the localization activity precisely. Moreover, it explores how fusion technologies like WSN, IoT, ML, and sensors will enhance localization accuracy and reliability in various agriculture activities. Additionally, the paper discusses the application of localization in the agriculture sector, such as crop monitoring, livestock management, precision agriculture, environmental conditions, and crop monitoring. It also discusses the challenges and obstacles in the path domain of various evaluation parameters like energy efficiency, scalability, and robustness, along with research and societal implications. Overall, the paper provides valuable guidance and outlines potential directions for future research in ML‐driven localization in WIoT for smart agriculture, offering a clear roadmap for researchers.
Singh et al. (Thu,) studied this question.