Design of a smart greenhouse environment monitoring and disease detection system

Abstract

In this study, we used the Internet of Things (IoT) and artificial intelligence to develop a smart greenhouse environment monitoring and disease detection system. We have developed an automated conveyor belt-type hardware system that works in conjunction with an Arduino Uno base central processing unit and a hybrid (solar and grid-based) power management system. The central processing unit is composed of temperature, humidity, light intensity, darkness, and soil moisture sensors. A high-quality camera was also mounted to the conveyor belt hardware to capture the images of plants and a display unit was also installed with a central processing unit to visualize the data in real-time. The YOLOV8 image processing was performed on a remote computer and the expected results were shown on an external monitor. The design of the proposed system helps us automatically monitor the greenhouse environment and detect disease in the observed plants. The solar-powered automated hybrid system not only saves money on electricity but also on labor-intensive maintenance. The current cutting-edge smart farm monitoring system will not only stimulate the next generation of farmers but will also aid the economic growth of agricultural-based countries such as Bangladesh.