Smart Agriculture Monitoring System Using IOT

Abstract— 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. Keywords— Smart Farming, IoT, ESP32, ThingSpeak, Machine Learning, Crop Recommendation, Precision Agriculture, Solar Energy.