A modern, AI-powered agricultural assistance platform that helps farmers make data-driven decisions for crop management and optimization.

• Smart crop suggestions based on soil composition, climate, and historical data
• Personalized recommendations considering local weather patterns
• Profit optimization based on market trends

• Custom fertilizer recommendations based on soil nutrients
• Balanced NPK ratio suggestions
• Cost-effective fertilizer planning

• Real-time weather forecasting
• Climate impact analysis
• Seasonal planning assistance

• State-wise profit projections
• Market trend analysis
• ROI calculations for different crops

• Responsive design for all devices
• Interactive dashboards
• Real-time data visualization
• User-friendly navigation
• Dark/Light mode support
• Mobile-first approach

• Backend: Python 3.9+
• Frontend: HTML5, CSS3, Modern JavaScript
• ML/AI: Scikit-learn, TensorFlow
• Data Processing: Pandas, NumPy
• Weather API Integration: OpenWeatherMap
• Visualization: Plotly, Chart.js

• Python 3.9 or higher
• pip package manager
• Virtual environment (recommended)

1. Clone the repository:

git clone https://github.com/Abs6187/Agro-AID.git
cd Agro-AID

2. Create and activate 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 API keys and configurations

5. Run the application:

python app.py

Run the test suite:

pytest

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

This project is licensed under the MIT License - see the LICENSE file for details.

• Created with ❤️ for Smart India Hackathon 2024
• Maintained by Abs6187

For support, email [contact2abhaygupta6187@gmail.com] or open an issue on GitHub.