IoT Based Smart Agriculture With Machine Learning

Abstract

This project presents an IoT and machine learning-based smart agriculture system designed to improve crop prediction, monitoring, and disease detection. The system uses sensors such as DHT11, soil moisture, and TDS sensors to collect real-time environmental data including temperature, humidity, soil moisture, and water quality. The collected information is transmitted through an ESP8266 Wi-Fi module to a cloud platform for storage and analysis. Machine learning algorithms process this data to predict the most suitable crop for cultivation based on current field conditions. In addition, the system supports image-based crop disease detection by analyzing leaf images through image processing and trained machine learning models. This helps identify plant diseases at an early stage and provides timely recommendations for preventive or corrective action. By combining IoT, cloud communication, machine learning, and image processing, the proposed solution enables farmers to make informed decisions, reduce crop losses, and improve productivity. The system also supports efficient resource use and promotes sustainable farming practices. Overall, this smart agriculture framework offers a cost-effective and reliable approach for precision farming, crop recommendation, and disease management in modern agriculture.