An IoT and Machine Learning-driven Advanced Greenhouse Farming System for Precision Agriculture

Abstract

With the world population projected to reach 9.8 billion by 2050, sustainable food production has become a significant concern. Adverse climatic changes and increasing pressure on food security have led to the search for innovative and effective agricultural methods. Traditionally, farming has not kept pace with increased demand without stressing the environment. The proposed system implements transformational agriculture through real-time monitoring and control infrastructure that picks up from the very basics of a greenhouse climate monitoring system using sensors to actuators. The new greenhouse system will be powered by solar energy—with a solar tracker—for running its operations and rainwater for irrigation, coupled with the trend of modernity in the form of a user-friendly mobile application. On this Monitoring Dashboard, there is the possibility of real-time control over temperature, humidity, light intensity, and soil moisture to arrive at optimal conditions for the crops. This system will be complete with a subsystem on crop recommendation and disease detection, making it comprehensive in agriculture. Rigorous simulations were performed on the model, and the resulting accuracy in crop recommendation and crop disease detection were 97.27% and 97.50%, respectively, quickly proving the effectiveness of smart greenhouse monitoring driven by IoT and machine learning. Such a solution can be expected to realize its objective: producing enough food for the increasing population without ruining planetary health.