AgriSense AI is an AI-powered smart farming platform that helps farmers predict crop yield, market price, and nutrient optimization strategies using machine learning, deep learning, and computer vision.
This project focuses on precision agriculture by combining soil data, weather conditions, crop images, and economic analysis to support data-driven farming decisions.
- Predicts expected crop yield based on:
- Crop type
- Soil nutrients (N, P, K, Mg)
- Soil pH
- Weather conditions (temperature, rainfall, humidity)
- Location (district/state)
- Estimates market price for the predicted yield
- Interactive nutrient sliders (N, P, K, Mg)
- Real-time yield variation analysis
- Helps farmers understand how nutrient changes affect productivity
- Upload crop leaf images
- Detects nutrient deficiencies:
- Nitrogen (N)
- Phosphorus (P)
- Potassium (K)
- Magnesium (Mg)
- Uses CNN-based image classification models
Provides two clear choices:
- Option A: Invest in nutrients → increased yield & profit
- Option B: No changes → baseline yield & income
Helps farmers make budget-aware decisions.
| Module | Model Type |
|---|---|
| Yield Prediction | Random Forest / XGBoost |
| Price Forecasting | Regression + Time Series |
| What-if Analysis | Feature sensitivity modeling |
| Deficiency Detection | CNN (ResNet / EfficientNet) |
- Programming: Python
- Backend: FastAPI
- Frontend: Streamlit / Flutter
- ML & DL: Scikit-learn, TensorFlow / PyTorch
- Computer Vision: OpenCV, CNNs
- Data Sources: Government agriculture & weather datasets