Nur2712/agricultural-advisory-bangladesh

Agricultural Advisory System for Bangladesh

A comprehensive machine learning system providing all features in one unified interface:

• —🌾 Crop Recommendations: Suggests optimal crops from 73 varieties based on environmental conditions
• —📊 Yield Predictions: Predicts crop yields using trained Random Forest models
• —🌤️ Weather Integration: Real-time weather forecasts for 8 Bangladesh districts via Open-Meteo API
• —🔮 Unified Recommendation Engine: Combines weather data, crop recommendations, and yield predictions

Features

🌾 Crop Recommendation

• —Get crop recommendations from 73 crop varieties
• —See top 5 recommendations with probability scores
• —Based on rainfall, temperature, humidity, and area

📊 Yield Prediction

• —Accurate yield predictions in metric tons
• —Uses Random Forest models
• —Real-time calculations

🌤️ Weather Forecast

• —7-day weather forecasts for 8 Bangladesh districts
• —Temperature, precipitation, and humidity data
• —Real-time data from Open-Meteo API

🔮 Unified Advisory

• —Complete advisory combining all features
• —Weather data integration
• —Crop recommendations with yield predictions
• —One-stop solution for agricultural planning

Supported Districts

• —Dhaka
• —Chittagong
• —Sylhet
• —Rajshahi
• —Khulna
• —Barisal
• —Rangpur
• —Mymensingh

How to Use

1. 1.Crop Recommendation Tab: Enter environmental conditions to get crop recommendations
2. 2.Yield Prediction Tab: Enter conditions to predict crop yields
3. 3.Weather Forecast Tab: Select district and get weather forecast
4. 4.Unified Advisory Tab: Get complete advisory with all features combined

API Endpoints

• —GET / - Interactive web interface
• —POST /predict/crop - Get crop recommendation
• —POST /predict/yield - Predict crop yield
• —GET /weather/{district}?days=7 - Get weather forecast
• —POST /advisory - Get complete unified advisory
• —GET /status - Check model status
• —GET /health - Health check

Models

Models automatically loaded from: Nur2712/agricultural-advisory

Note

Models are trained on Bangladesh-specific agricultural data. Results may vary for other regions.