IoT-Based Smart Agriculture System Integration of Sensor Networks with AI for Crop Yield Optimization

The application of IoT sensor networks based on AI devices in agriculture enables monitoring of the systems and predictive management to determine optimum usage of resources real-time. This paper introduces a smart agriculture system that covers a system that uses a sensor-based monitoring technique, edge computing, and an AI-based crop yield prediction algorithm for improvement of efficient crop production. The system utilizes a process flow at the system level involving data acquisition from the sensors, artificial intelligence decision-making component, and closed feedback loop for high precision farming. Explorations conclude that there is 92% enhancement in the chances of crop yield prediction, 40% enhancement in the use of resources and they also show 35% reduction in operation costs. Integration of edge computing lowers the processing time to 38% hence the ability to attend to crop health complications instantly. These provide an endorsement of IoT & AI integration in the development of efficient and profitable smart farming innovation within a controlled decision-making framework.