Environmental Monitoring and Control System of Green House with Microcontroller and GSM Using IoT Devices

This paper presents a comprehensive solution for monitoring and controlling greenhouse environments using microcontroller technology and GSM connectivity through IoT devices. The system integrates various sensors to collect real-time data on environmental parameters such as temperature, humidity, soil moisture, and light intensity within the greenhouse. A microcontroller unit processes the sensor data and executes control actions to optimize environmental conditions for plant growth. Through GSM connectivity, the system enables remote monitoring and control, allowing users to access greenhouse data and adjust settings from anywhere using a mobile device or computer. The proposed system offers efficient management of greenhouse conditions, leading to improved crop yield, reduced resource consumption, and enhanced sustainability in agriculture. Continuing from the abstract, the system architecture employs a microcontroller, such as Arduino or Raspberry Pi, as the central processing unit. The microcontroller interfaces with a variety of sensors including temperature sensors, humidity sensors, soil moisture sensors, and light sensors to continuously monitor the greenhouse environment. Data collected from these sensors are processed in real-time by the microcontroller to make informed decisions regarding environmental control parameters such as irrigation, ventilation, and lighting. Control actions are executed autonomously based on predefined thresholds and algorithms to maintain optimal growing conditions for the plants. The inclusion of GSM connectivity enables the system to communicate data and receive commands remotely. Users can access the greenhouse monitoring system through a web or mobile application, providing real-time updates on environmental conditions and allowing for remote control of the greenhouse parameters. Furthermore, the integration of IoT devices allows for scalability and flexibility in the system architecture, facilitating the addition of new sensors or control mechanisms as needed. Additionally, data analytics and machine learning techniques can be implemented to further optimize the system's performance and predictive capabilities. Overall, the proposed monitoring and control system offers a cost-effective, efficient, and sustainable solution for greenhouse management, with potential applications in commercial agriculture as well as small-scale or hobbyist setups