AgriSathi is an AI-based smart agriculture advisory system designed to assist farmers in making informed decisions about crop selection and fertilizer usage.
The system uses machine learning techniques to provide data-driven recommendations based on soil and environmental conditions.
- Reduce dependency on guesswork
- Improve productivity
- Promote sustainable farming practices
Predicts the most suitable crop based on:
- Nitrogen (N)
- Phosphorus (P)
- Potassium (K)
- Temperature
- Humidity
- pH value
- Rainfall
Built using:
- Random Forest Classifier
Recommends appropriate fertilizers based on:
- Soil nutrient composition
- Crop requirements
Helps in:
- Improving soil health
- Reducing excessive fertilizer usage
- Lowering farming costs
- Python
- Flask
- Scikit-learn (RandomForestClassifier)
- Pandas
- NumPy
- HTML
- CSS
- JavaScript
- Bootstrap
- User enters soil and environmental data through the web interface
- Frontend sends request to Flask backend
- Backend processes data using trained ML models
- Prediction results are returned to the user
- Improve crop yield
- Reduce farming costs
- Promote sustainable agriculture
- Provide AI-powered assistance to farmers
- 🌦️ Weather-based advisory system
- 🐛 Plant disease detection (image-based)
- 🌐 Multilingual support
- 🗣️ Voice interaction
- 💰 Market price tracking
- Small and marginal farmers
- Agricultural advisors
- Agri-tech developers
- Contributions are welcome
- Feel free to fork the repository
- Submit a pull request
- This project is open-source and available under the MIT License
- If you found this project useful, please give it a star on GitHub