IOT and machine learning driven greenhouse framework for smart and sustainable agriculture

The Internet of Things (IoT) and Machine Learning (ML) driven greenhouse framework is designed to enhance smart and sustainable agriculture by enabling automated monitoring and control of environmental conditions. In this system, an Arduino microcontroller is used to interface and control multiple sensors, including the DHT11 sensor for measuring temperature and humidity, a soil moisture sensor for detecting soil water levels, and a pH sensor for monitoring water quality. The collected data is processed and used to control actuators such as a relay module and a DC water pump, ensuring optimal irrigation and environmental balance inside the greenhouse. Furthermore, machine learning techniques, particularly the Random Forest algorithm, are integrated to analyze historical sensor data and predict environmental conditions, enabling intelligent decision-making for crop health and resource optimization. This framework improves efficiency, reduces manual intervention, and supports sustainable agricultural practices by maintaining ideal growth conditions automatically.