AgriMitra is a smart agricultural companion application designed to empower farmers with data-driven insights. By analyzing real-time climate data against crop requirements, AgriMitra helps farmers understand the success rate of common crops, identify prevalent crop diseases, and find effective cures.
- Crop & Disease Intelligence: Access a comprehensive database of common crops, their widespread diseases, and proven cures/treatments.
- Climate Analysis: Fetches current local weather data and compares it against the ideal climate thresholds required for specific crops.
- Crop Success Rate: Uses an algorithm to calculate and display a predictive success percentage for crops based on real-time environmental factors.
- Dual Location Selection:
- GPS Mode: Automatically fetches high-accuracy real-time geographic coordinates.
- Manual Mode: A built-in curated list of major states and their important agricultural cities for quick selection.
Based on your repository, here is how the core files interact:
app.js/index.html- The frontend user interface for selecting locations and viewing success rates.app.py- The backend Python service handling climate data calculations and crop algorithms.crops.json- Database containing crop thresholds, diseases, and cure data.places.json- Pre-configured list of supported states and major cities.requirements.txt- Python dependencies needed to run the backend application.
Make sure you have both Node.js (for frontend/fullstack JS execution) and Python 3.x installed.
git clone [https://github.com/lokeshchevuri/Agrimitra.git]
(https://github.com/lokeshchevuri/Agrimitra.git)
cd Agrimitra
2. Set Up the Python BackendInstall the required dependencies listed in requirements.txt:Bashpip install -r requirements.txt
3. Run the ApplicationLaunch your backend application:
Bash
python app.py
📊 How it Works (Core Logic)Location Input: The farmer grants GPS permission or selects their region from the dropdown menu.Weather Fetching: The app sends the coordinates to a weather API to retrieve live parameters (Temperature, Humidity, Rainfall).
Success Rate Calculation:
The app compares live metrics against the boundaries stored in crops.json
Actionable Insights:
The system outputs a percentage score along with custom preventative measures for common crop diseases.
🤝 Contributing
Contributions, issues, and feature requests are welcome!
Feel free to check the issues page if you want to help expand the crops.json database or improve the climate matching algorithm.
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