ISSN: 2996-671X
Authors: Md. Al Mamun*
In-depth analysis of the most recent developments in IoT-based agriculture and smart farming is provided in this article with a specific focus on the integration of machine learning applications. With the advent of Internet of Things (IoT) technology, conventional agricultural methods have been transformed by the ability to gather real-time data from numerous sensors and devices. The commentary highlights the pivotal role of machine learning in harnessing the vast amounts of data generated by IoT devices. By employing sophisticated machine learning algorithms, farmers can analyse historical and real-time data to uncover valuable insights, forecast trends, and proactively manage their farms. Applications of machine learning in agriculture, such as precision agriculture, automated monitoring, and predictive maintenance, have contributed to increased efficiency, optimized resource utilization, and higher crop yields. While delving into the challenges and opportunities, the commentary emphasizes the significance of data privacy and security in IoT-based agriculture. Furthermore, it discusses the integration of machine learning at the edge, which facilitates rapid and decentralized decision-making, minimizing latency and dependence on cloud-based solutions. The potential societal and environmental impacts of adopting IoT-based smart farming techniques are also discussed, as these applications promote sustainable practices, resource conservation, and food security
Keywords: Machine Learning (ML); Internet of Things (IoT); Smart Farming; Machine Learning with IoT (MLIoT); Smart Agriculture
