This research explores the application of Artificial Intelligence (AI) in the Internet of Things (IoT) for crop yield prediction in agriculture. IoT devices, like sensors and drones, collect data on temperature, humidity, soil moisture, and crop health. AI algorithms process and integrate this data to provide a comprehensive view of the agricultural environment.AI-driven anomaly detection helps identify threats to crop yield, such as pests, diseases, and adverse weather conditions. Predictive analytics, based on historical and real-time data, forecast crop yield for informed decision-making in irrigation and fertilization.AI-powered image recognition detects early signs of pests and diseases, aiding timely treatment to prevent crop losses. Resource optimization allocates water and fertilizers efficiently, minimizing waste and environmental impact.AI-driven decision support systems offer personalized recommendations for ideal planting schedules and crop rotations, maximizing yield. Autonomous farming integrates AI into machinery for precision tasks like planting and monitoring.Secure communication protocols protect sensitive agricultural data from cyber threats, ensuring data integrity and privacy
