An IOT Application Based on Hybrid Machine Learning to Monitor, Assess, and Improve Agriculture Systems

The Internet of Things (IoT) technology has made everything smarter, which has transformed every facet of daily life. Among the many IoT applications, IoT-based smart agriculture has piqued the interest of many researchers, who have employed Machine Learning (ML) and IoT technology to achieve cutting-edge research. Data-driven farm management approaches based on IoT may assist enhance agricultural yields by managing input costs, minimizing losses, and making better use of resources. The Internet of Things creates a large volume of data with varying properties dependent on location and time. To agricultural output via intelligent farm management, data must be correctly evaluated and processed. As the quantity of data gathered grows, high-performance computing capabilities in ML offers up new options for data-intensive research; ML methods might be used to further improve application intelligence and usefulness. In this post, we will look at current methods to smart agriculture and farming that are based on IoT and ML independently. We also suggest innovative ideas for how ML-IoT might be integrated into such applications.