An AI-powered IoT ecosystem for precision agriculture in hilly terrains
Traditional precision agriculture systems assume flat farmland and uniform sensor deployment. These assumptions become inefficient in mountainous regions where:
- Terrain creates communication shadow zones
- Water sources are scarce despite high humidity
- Weather varies significantly within small geographical regions
- Conventional sensor placement results in redundant hardware and higher costs
SmartAgro addresses these challenges through:
- Optimized sensor deployment algorithms
- ESP32 mesh networking
- Hyper-local weather prediction
- AI-assisted irrigation scheduling
- Fog water harvesting
- Real-time farmer monitoring applications
- Soil moisture monitoring
- Soil temperature monitoring
- Ambient temperature monitoring
- Atmospheric pressure monitoring
- Continuous micro-climate analysis
- ESP32-based sensor nodes
- Mesh networking
- Hexagonal tessellation deployment
- Terrace-aware linear node placement
- Hardware fault detection
- Low-power operation
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Hybrid XGBoost prediction model
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Uses:
- Open-Meteo forecasts
- Local aerodrome weather data
- Real-time farm sensor data
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Predicts rainfall for the next hour
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Achieves approximately 97% local rain/no-rain prediction accuracy
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Automatically pauses irrigation when rainfall is expected
- Sector-wise irrigation control
- Multi-valve architecture
- Automated pump control
- Crop-specific irrigation scheduling
- Water logging detection
- Leak detection
Built using:
- Kotlin
- Jetpack Compose
Features:
- Real-time monitoring
- Live sensor dashboard
- Manual irrigation control
- Emergency stop
- QR-based node onboarding
- Historical analytics
- Push notifications
Provides administrative and large-scale monitoring capabilities:
- Farm monitoring
- Sensor management
- Historical data visualization
- Government/resource monitoring
- Water usage analytics
- Remote management
Powered by Firebase:
- Real-time Database
- Authentication
- Historical storage
- Device synchronization
- Secure cloud communication
Input data includes:
- Open-Meteo forecast
- Local aerodrome weather
- Humidity
- Pressure
- Wind speed
- Wind direction
- Historical farm sensor readings
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Feature Engineering
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XGBoost Training
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Rainfall Prediction
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Automated Irrigation Decision
Sensors
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ESP32 Soil Node
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Mesh Network
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Parent Node
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Firebase Cloud
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Android Web Dashboard
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Weather Prediction Model
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Smart Irrigation Decisions