Agri Sentinel AI: Revolutionizing Smart Agriculture with Real-Time Disease Detection and Advisory Systems

Agriculture remains the backbone of many developing economies, yet it is increasingly threatened by plant diseases, insect infestations, and animal intrusions that lead to significant yield losses and economic instability for farmers. Conventional crop monitoring still depends largely on manual field visits and occasional expert supervision. This approach is labour intensive, slow, and unsuitable for continuous large-scale surveillance. In the early stages, symptoms of diseases such as rust, blight, or bacterial infections are often faint and easily overlooked, allowing problems to spread before any action is taken. To resolve these issues, this work introduces Agri Sentinel AI, an intelligent, real-time agricultural monitoring system that combines deep learning, computer vision, and Internet of Things (IoT) technologies. Low cost ESP32 camera modules are installed across the field to continuously capture live images of crops. These image streams are processed by YOLO-based object detection models capable of identifying plant diseases, pest activity, and animal intrusions under diverse environmental conditions. When a potential threat is spotted, the system produces labelled images highlighting detected areas and confidence levels, and links each detection to an integrated agricultural knowledge base. This knowledge base provides detailed disease descriptions, prevention strategies, and recommended treatments. The relevant information, along with the annotated image, is immediately delivered to farmers via Telegram alerts, enabling quick, informed responses. Field experiments conducted in real agricultural environments indicate that the system is accurate, scalable, and affordable, ensuring its suitability for smart farming and precision agriculture.