Article
AUTOMATED PEST AND DISEASE DETECTION FOR AGRICULTURE
The Automated Pest and Disease Detection System leverages advanced computer vision and deep learning to mitigate the limitations of manual agricultural monitoring, such as human error and delayed intervention. By integrating Convolutional Neural Networks (CNN) for precise disease classification and the YOLO (You Only Look Once) framework for real-time pest localization, the system transforms raw crop imagery into actionable diagnostic data. This automated approach facilitates early-stage detection and targeted pesticide application, significantly reducing economic losses while promoting environmental sustainability. Ultimately, the project bridges the gap between expert knowledge and field-level implementation, providing a scalable solution for modernizing global food security through data-driven, precision agriculture.
Full Text Attachment
