FarmerAI-
🌾 Farmer AI — Professional Smart Crop Intelligence Platform
Farmer AI is a high-performance, professional-grade agricultural intelligence platform designed to empower farmers with data-driven decision-making. It combines advanced machine learning models, real-time data integration, and an intelligent AI advisory system within a premium, SaaS-style user interface.
🚀 Live Demo
Check out the live website here: https://SajidAnsari786.github.io/FarmerAI-/
(Note: The live demo runs in Demo Mode. For full AI functionality, please run the local backend.)
✨ Key Features
1. Smart Crop Recommendation
- Predictive Engine: Powered by a Random Forest Classifier trained on 5,430+ rows of high-fidelity agronomic data.
- Coverage: Supports 48 diverse Indian crops including cereals, pulses, oilseeds, fruits, vegetables, spices, and plantation crops.
- Accuracy: Achieves ~88% accuracy with 5-fold cross-validation.
2. Precision Yield Estimation
- Yield Forecasting: Integrated XGBoost Regressor for precise yield prediction based on soil conditions and farm area.
- Revenue Projection: Automatically calculates estimated revenue based on current market prices and predicted yield.
3. Intelligent AI Advisor
- Knowledge Base: Context-aware AI advisor trained on professional agronomic practices.
- Expert Guidance: Get instant answers on pest control, fertilizer application, and soil health management.
4. Real-Time Market & Weather
- Market Prices: Live MSP data for 2024-25 and mandi price trends across India.
- Weather Dashboard: Hyper-local weather forecasting to plan irrigation and harvesting.
🛠️ Technology Stack
| Component | Technology |
|---|---|
| Frontend | React, Lucide Icons, Vanilla CSS (Premium Glassmorphism) |
| Backend | FastAPI (Python), Uvicorn |
| Machine Learning | Scikit-Learn (Random Forest), XGBoost |
| Data Processing | Pandas, NumPy, Joblib |
| Deployment | GitHub Pages (Frontend), Cloud Run / Local (Backend) |
📦 Installation & Setup
1. Clone the Repository
git clone https://github.com/SajidAnsari786/FarmerAI-.git
cd FarmerAI-
2. Setup Backend
cd backend
pip install -r requirements.txt
python train_model.py # To generate the ML models
python main.py # To start the API server
3. Setup Frontend
cd frontend
npm install
npm run dev
📊 Model Performance
- Crop Model Accuracy: 87.66%
- Yield Model R² Score: 97.8%
- Supported Crops: 48 Varieties
🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
📄 License
This project is licensed under the MIT License.