IoT Based Smart Farming System using Machine Learning

This research delivers an Internet of Things-enabled smart farming system that leverages AI capabilities for better agricultural management. The system includes manual NPK value input in addition to a number of sensors, including as flame, DHT, and soil moisture sensors, for real-time monitoring of environmental conditions and soil health. The YOLO model and an image detection system with OpenCV are also used in the assessment of crop health. Crop recommendations are facilitated by a Random Forest classification system, which is based on sensor data and picture analysis results. The Raspberry Pi serves as the central processing unit, coordinating the processes of data collection, analysis, and decision-making. By providing farmers with practical information, the system aims to maximize output, minimize resource usage, and promote sustainable farming practices. Farmers can lessen risks such as diseases, pests, and other problems by keeping a close eye on things and analyzing bad weather patterns.