AI-Powered Identification of Corn Leaf Diseases and Pests: A Flask-Based Web Application for Enhanced Agricultural Decision-Making

As the global population continues to grow, the demand for food escalates, placing corn—a vital staple crop—at risk from diseases and pests. To address this challenge, we have developed a corn leaf disease and pest identification system that leverages artificial intelligence and big data analytics. Built using the Flask framework, this user-friendly web application employs the YOLOv9 model for accurate target detection and identification. The system features key functionalities such as image upload and recognition, user registration and login, and detailed reports on diseases and pests, allowing users to easily access and query relevant information for timely action against identified threats. Experimental results demonstrate that the system achieves high accuracy and stability in identifying corn leaf diseases and pests, providing farmers with a crucial tool to protect their crops. By facilitating informed decision-making, this system significantly contributes to enhancing corn production efficiency and supporting sustainable agricultural practices.