🍃 Advanced Mango Disease Detection Using YOLO + Edge Enhancement

This project implements a YOLO-based deep learning model to detect and classify mango leaf diseases with high accuracy. To improve feature extraction, a Canny edge-enhanced channel is added to the input, helping the model focus on leaf texture, disease boundaries, and infection patterns.

To further boost performance, Grey Wolf Optimization (GWO) is used for hyperparameter tuning—resulting in significantly better accuracy and model stability. The system is designed to support early detection, helping farmers and agricultural teams take timely action.

🔍 Key Features

YOLO-based real-time mango disease detection

Additional Canny edge channel for improved feature extraction

GWO-based hyperparameter optimization

Custom dataset preprocessing pipeline

High accuracy and improved interpretability

🛠 Tech Stack / Skills Used

Python, OpenCV, YOLO, Canny Edge Detection, Grey Wolf Optimization (GWO), Deep Learning, Computer Vision, Image Preprocessing, Model Training & Evaluation