PlantSense 🌿

PlantSense is an AI-powered plant disease detection and diagnosis system that democratizes access to expert plant pathology knowledge. By combining computer vision and conversational AI, PlantSense provides:

1. Instant Disease Detection: Using advanced deep learning models trained on the Plant Village dataset, PlantSense can instantly identify 35+ common plant diseases from a single photo with over 92% accuracy.

2. Expert-Level Insights: Through integration with OpenAI's language models, PlantSense provides detailed, contextual information about:

   • Disease characteristics and progression
   • Environmental factors and conditions
   • Prevention strategies
   • Treatment recommendations
   • Long-term management practices

3. Interactive Guidance: Users can engage in natural conversation with the AI to:

   • Ask follow-up questions
   • Get clarification on treatments
   • Receive customized care instructions
   • Learn about prevention methods

Features

• Real-time Plant Disease Detection: Upload images of your plants for instant disease detection
• Interactive Chat Interface: Get detailed insights and treatment recommendations through a conversational AI
• Image History: Access your previously uploaded images and their diagnoses
• High Accuracy: Powered by a fine-tuned ResNet model achieving 92%+ accuracy
• Responsive Design: Clean, modern interface that works across devices

Technology Stack

Backend

• Flask (Web Framework)
• SQLAlchemy (ORM)
• PyTorch (Deep Learning)
• OpenAI API (Chat Insights)
• SQLite (Database)

Frontend

• HTML5/CSS3
• JavaScript
• Jinja2 Templates

ML/AI

• ResNet18 (Pre-trained Model)
• Custom Training Pipeline
• Plant Village Dataset

Installation

1. Clone the repository:

   • As the repository is private, you need to have access to the repository from the owner or a collaborator.

   git clone https://github.com/yourusername/plantsense.git
   cd plantsense

2. Create and activate a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables:

   cp .env.example .env
   # Edit .env with your configurations

5. Initialize the database:

   flask db upgrade

Usage

1. Start the Flask development server:

   python run.py

2. Open your browser and navigate to http://localhost:5000

3. Upload a plant image through the interface

4. View the diagnosis and chat with PlantSense for detailed insights

Model Training

The project uses a custom-trained ResNet18 model for plant disease detection. To train the model:

1. Download the Plant Village dataset

2. Run the training script:

   python scripts/train.py

Training configurations can be modified in src/training/trainer.py.

Development

Database Migrations

flask db migrate -m "Migration message"
flask db upgrade

Adding New Features

1. Create a new branch:

   git checkout -b feature/your-feature-name

2. Make your changes

3. Run tests (if applicable)

4. Submit a pull request

API Reference

Image Upload Endpoint

• POST /: Upload plant images for analysis
• Response: Redirects to chat interface with diagnosis

Chat Endpoint

• POST /chat: Submit user queries
• Response: AI-generated insights and recommendations

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

Acknowledgments

• Plant Village Dataset for training data

  • Dataset Link: https://data.mendeley.com/datasets/tywbtsjrjv/1

• OpenAI for chat capabilities

• PyTorch team for the deep learning framework

• Flask team for the web framework

Contact

For questions and support, please open an issue on the GitHub repository.