AI BASED EMBEDDED SYSTEM APPROACH TO MONITORING GREEN HOUSE

Abstract

An AI-based Embedded System Approach to Monitor Greenhouse is designed to provide an efficient, intelligent, and automated solution for maintaining optimal environmental conditions for plant growth. This project uses an Arduino microcontroller as the central control unit to continuously monitor and regulate greenhouse parameters, while integrating Machine Learning techniques for smart decision-making and predictive control. A DHT11 sensor is used to measure temperature and humidity levels inside the greenhouse environment. MQ2 and MQ6 gas sensors are integrated to detect harmful gases and ensure safe air quality for plants. A soil moisture sensor monitors the water content in the soil and supports proper irrigation management. All sensed values are displayed on an LCD for real- time monitoring and user awareness. The system ensures continuous monitoring and efficient control of greenhouse conditions with minimal human intervention. It helps in improving crop yield by maintaining optimal environmental parameters. The integration of AI and IoT technologies makes the system more reliable and suitable for modern smart agriculture applications.It enables real-time data monitoring and quick decision-making for better environmental control. The system also enhances reliability and reduces the risk of crop damage.