🌱 AI-Powered Plant Disease Detection 🌿

This project applies machine learning to identify plant diseases with high accuracy. By leveraging algorithms like Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Naïve Bayes, we analyze plant images to detect signs of infection.

How It Works

• Feature Extraction: Techniques like Histogram of Oriented Gradients (HOG) and wavelets help identify crucial visual patterns in plant images.
• Model Training: Extracted features are fed into multiple classifiers to evaluate their performance in disease detection.
• Key Insight: Among the tested models, SVM combined with color-based features delivers the most accurate results.

Why It Matters

Early detection of plant diseases enables farmers to take preventive action, improving crop yield and overall plant health. This project aims to contribute to smarter, data-driven agriculture for a more sustainable future.