Smart Agriculture Monitoring System Using IOT

The increasing demand for efficient and sustainable agricultural practices has led to the adoption of smart technologies in farming. This project presents a Smart Agriculture Monitoring System that integrates Internet of Things (IoT), renewable energy, and machine learning to provide real-time monitoring and decision-making support for farmers. The system utilizes sensors such as DHT11 for temperature and humidity, a rain sensor, and other environmental monitoring modules connected through an ESP32 microcontroller. Sensor data is transmitted wirelessly and visualized on ThingSpeak, a cloud-based platform, allowing farmers to remotely track field conditions and take timely actions. A key feature of the system is the Plant Recommendation System is a web-based application that suggests suitable plants based on current weather data. It collects real time information such as temperature, humidity, soil moisture, and rainfall from a ThingSpeak channel. This data is processed using a machine learning model built with the Random Forest algorithm. The model predicts the best plant type for the given conditions. The application is developed using Flask and features a user- friendly interface designed with modern CSS styling. The system helps users, especially farmers and gardeners, to make better planting decisions based on data-driven insights. The system is powered by solar energy, ensuring sustainability and suitability for rural or off-grid areas. Overall, the Smart Agriculture Monitoring System offers a cost-effective, scalable, and eco-friendly solution for modern precision agriculture, paving the way for future integration of automation and advanced analytics in farming.