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--- |
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title: CDIApp |
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emoji: 🏆 |
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colorFrom: red |
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colorTo: blue |
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sdk: gradio |
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sdk_version: 5.4.0 |
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app_file: app.py |
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pinned: false |
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license: apache-2.0 |
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--- |
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# Plant Disease Classification and Remedy System |
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This is a deep learning project for classifying plant diseases from images. It uses a convolutional neural network trained on a dataset of plant disease images. |
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A deep learning-based system that identifies plant diseases from images and provides remedies using AI. |
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## Features |
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- 🌿 Identifies 38 different plant diseases across various crops |
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- 🔍 Uses advanced deep learning models for accurate disease detection |
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- 🤖 Integrates with Gemini AI for providing detailed remedies |
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- 🎯 Includes Out-of-Distribution detection for reliable predictions |
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- 🖥️ User-friendly Gradio web interface |
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## System Requirements |
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- Python 3.8+ |
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- CUDA-compatible GPU (optional, for faster inference) |
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- 4GB+ RAM |
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## Installation |
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1. Clone the repository: |
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2. Install dependencies: |
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```bash |
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pip install -r requirements.txt |
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``` |
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3. Set up environment variables: |
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Create a `.env` file in the root directory and add: |
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``` |
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GEMINI_API_KEY=your_gemini_api_key_here |
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``` |
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## Project Structure |
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``` |
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├── acfg/ # Application configuration |
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├── ml/ # Machine learning modules |
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│ ├── app/ # Core ML application code |
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│ └── input/ # Dataset directory |
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├── service/ # Backend service |
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└── static/ # Model checkpoints and static files |
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``` |
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## Usage |
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### Running the Web Interface |
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```bash |
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python app.py |
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``` |
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Access the interface at `http://localhost:7860` |
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### Training Models |
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1. Train the classification model: |
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```bash |
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python train_classifier.py |
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``` |
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2. Train the OOD detection model: |
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```bash |
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python train_ood.py |
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``` |
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## Model Architecture |
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- **Classification**: MobileNetV3 Small with custom MLP head |
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- **OOD Detection**: Custom Autoencoder architecture |
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- **LLM Integration**: Gemini 1.5 Flash for remedy generation |
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## Dataset |
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The system is trained on a comprehensive dataset of plant disease images, covering: |
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- Multiple crop types (Apple, Corn, Tomato, etc.) |
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- Various disease conditions |
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- Healthy plant samples |
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## Contributing |
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1. Fork the repository |
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2. Create your feature branch (`git checkout -b feature/amazing-feature`) |
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3. Commit your changes (`git commit -m 'Add some amazing feature'`) |
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4. Push to the branch (`git push origin feature/amazing-feature`) |
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5. Open a Pull Request |
