🌱 Plant Disease Detection

📌 Overview

The Plant Disease Detection project uses Convolutional Neural Networks (CNNs) to classify plant leaves as healthy or diseased.
It helps farmers and researchers identify plant diseases early and take preventive measures.

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🚀 Features

• Image-based plant disease classification.
• Trained on a large plant leaf dataset with multiple disease categories.
• Interactive Hugging Face demo with image upload support.
• High accuracy using deep learning (CNN).

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🗂 Dataset

The dataset used is the PlantVillage dataset, which contains thousands of labeled images of healthy and diseased plant leaves.

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🛠️ Technologies Used

• Python 🐍
• TensorFlow / Keras
• OpenCV
• NumPy & Pandas
• Matplotlib & Seaborn
• Hugging Face Spaces + Gradio (for live demo)

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📸 Screenshots

(Add your screenshots here: training results, confusion matrix, Hugging Face demo UI)

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🔴 Live Demo

👉 Try the model on Hugging Face: Plant Disease Detection Demo

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📈 Results

• Achieved high classification accuracy on test images.
• Correctly detects multiple plant diseases.
• Reliable predictions even on unseen leaf samples.

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🔮 Future Improvements

• Extend to more plant species.
• Deploy as a mobile app for farmers.
• Add real-time webcam-based detection.

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👨‍💻 Author

Muhammad Rayan Shahid
AI & ML Enthusiast

🌐 GitHub
💼 LinkedIn
📊 Kaggle
🤗 Hugging Face
🎥 YouTube - ByteBrilliance AI

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