IoT Based Precise Greenhouse Management System using Machine Learning Algorithm

The Internet of things (IoT) allows for previously impossible interaction between previously unlike objects. The implementation of IoT is essential to the success of the agricultural industry. Using these techniques, farmers can increase their crop yields while using fewer resources. However, most farmers still lack knowledge of modern techniques. Using IoT capabilities, precision, and intelligent agriculture manages agricultural production to improve crop quality by delivering the necessary nutrients. It reduces environmental damage caused by the use of excessive pesticides. In this research, the IoT and ML is used as a foundation to investigate the agricultural sustainable development platform to enhance the platform's management effect. This research described a precise and intelligent agricultural system that uses supervised classification and regression-based machine learning to analyse predicted data on sensing parameters. The fog, edge, and sensor layers are the three crucial parts of the suggested methodology. For controlling the actuators in the system, the application of machine learning on sensor data gathered from prototype embedded models is being examined. Then, at the fog layer, an analytics and decision-making system was developed using two supervised machine learning techniques, including classification and regression algorithms that made use of decision trees (DT) and multilayer perceptron neural networks (MLPNN) for efficient processing. When the experimental findings are examined and analysed, it is discovered that DT has a significantly higher classification accuracy than MLPNN and other cutting-edge techniques.