A Machine Learning Based IoT Fog -Enabled Framework for Green House Farming

By improving productivity, reducing production costs, and maximizing resource utilization, the Internet of Things (IoT) has revolutionized smart agriculture. In this article's examination of the future of automation, the potential uses of sensors and IoT in greenhouse farming are highlighted. Through a variety of sensors, it is possible to monitor various factors such as humidity, pH and EC values, temperature, UV light intensity, and CO2 level. This information is useful for early fault detection and diagnosis. The use of fog computing to support and improve cloud computing is on the rise. It enables faster service delivery to customers and enhances the IoT devices' quality of service. As an outcome, user-centric IoT-based applications are becoming more and more crucial. In this work, we proposed an IoT based Fog enabled Decision Support System which uses an energy-conscious allocation technique for allocating application tasks to fog devices. Energy and cost performance matrix is considered and compared with the existing approaches. The simulation result shows that from the traditional approach the proposed machine learning approach has 32% more energy aware and 35% cost effective.