Ml-Based Smart Greenhouse Monitoring And Control System Using Iot And Embedded Sensors

Abstract

This paper introduces a Smart Greenhouse Monitoring and Control System that uses IoT, embedded sensors, and machine learning to create a healthy and automated environment for plant growth. The system continuously measures key parameters such as temperature, humidity, soil moisture, and gas levels. Processes the data collected with a machine learning model that predicts if current conditions are suitable for plant growth. Based on these predictions, the ESP32 microcontroller automatically controls devices such as the water pump, fan, and heater to maintain the correct environment inside the greenhouse. The IoT feature allows users to monitor real-time data and control actions remotely through an online dashboard. This reduces manual work and ensures timely adjustments, even without physical supervision. The trained model improves decision making by learning from real-time sensor data. The main goal of this project is to reduce human effort, save resources, and provide a smart and efficient solution for modern agriculture. By combining IoT automation with machine learning, the system supports sustainable farming practices and ensures better plant productivity with less maintenance and cost.