AquaScribe is a next-generation IoT-based water management system tailored for precision farming. By combining real-time sensor data with machine learning (ML) algorithms, AquaScribe optimizes irrigation, reduces water wastage, and enhances crop productivity, all while offering seamless user control through a modern web dashboard.
- 🌱 Real-time Sensor Monitoring: Monitor soil moisture, temperature, and humidity levels with precise IoT sensors.
- 🤖 AI-driven Irrigation: Uses a Random Forest machine learning model (91.7% accuracy) to determine the need for irrigation based on environmental data.
- 💧 Automated Irrigation: Automatically controls water pumps based on sensor data and machine learning predictions.
- ☁️ Weather Dashboard: Provides real-time weather updates and forecasts using OpenWeatherMap API to further optimize irrigation.
- 📊 Data Visualization: Graphical representation of sensor data for easy analysis.
- 📱 Mobile Friendly: Responsive design for monitoring and controlling irrigation from mobile devices.
| Component | Technology |
|---|---|
| Frontend | React.js, HTML, CSS, JavaScript |
| Backend | Node.js, Express.js |
| Database | MongoDB |
| Hardware | Arduino Uno, DHT22 Sensors, Soil Moisture Sensors, Wi-Fi Modules |
| Machine Learning | Python, Random Forest Algorithm |
| Weather Data | OpenWeatherMap API |
- The sensors collect data on soil moisture, temperature, and humidity.
- This data is sent to the backend and stored in a MongoDB database.
- The Random Forest model processes the data to predict irrigation needs.
- Based on the model's output, the system automatically turns the irrigation pump on or off.
- Users can monitor the system through a real-time dashboard showing sensor data and predictions.
The Random Forest model is trained on the following features:
- Temperature
- Soil Moisture
- Humidity
- Pump status (on/off)
The model predicts whether irrigation is needed (yes/no) based on these inputs.
The AquaScribe dashboard displays:
- Real-time sensor data: Soil moisture, temperature, and humidity levels.
- Irrigation status: Whether irrigation is currently active or not.
- Weather updates: Current weather conditions and forecasts.
- Data graphs: Visual representation of sensor data over time.
- Water Usage Analytics: Track total water usage over time.
- Advanced ML Models: Integrating deep learning models for more accurate predictions.
- Weather-Based Irrigation Control: Modify irrigation based on weather forecasts.
Contributions are welcome! Please fork this repository and create a pull request.