System Architecture
The BAYYTI-B1 is a solar-powered, AI-enabled irrigation controller designed for off-grid agricultural applications. Built on Raspberry Pi 4 with custom hardware extensions, it provides enterprise-grade reliability at an affordable price point.
Hardware Specifications
Core Components
- Processor: Raspberry Pi 4 (4GB RAM)
- Storage: 32GB Industrial-grade SD Card
- Power: 50W Solar Panel + 12V 20Ah Battery
- Connectivity: 4G LTE, WiFi, Bluetooth 5.0
- Enclosure: IP67 Weatherproof
Sensor Inputs
- 8x Soil Moisture Sensor Ports (Capacitive)
- 4x Temperature/Humidity Sensor Ports (DHT22)
- 2x Flow Meter Inputs
- 2x Pressure Sensor Inputs
- 1x Rain Sensor Input
Control Outputs
- 8x Solenoid Valve Outputs (24V AC, 10A)
- 4x Pump Control Relays (240V AC, 30A)
- 2x Auxiliary Outputs
Software Architecture
class IrrigationController:
def __init__(self):
self.sensors = SensorManager()
self.valves = ValveController()
self.ai_engine = AIOptimizer()
self.cloud = CloudConnector()
async def run_irrigation_cycle(self):
# Read all sensors
sensor_data = await self.sensors.read_all()
# Get AI recommendations
recommendations = self.ai_engine.optimize(sensor_data)
# Execute irrigation
for zone in recommendations['zones_to_water']:
await self.valves.open(zone['id'], zone['duration'])
# Log to cloud
await self.cloud.log_event(sensor_data, recommendations)
AI Optimization Engine
The BAYYTI-B1 uses TensorFlow Lite for on-device AI inference, analyzing multiple factors:
Input Parameters
- Current soil moisture levels (8 zones)
- Soil temperature and ambient temperature
- Humidity levels
- Weather forecast (next 48 hours)
- Historical irrigation data
- Crop type and growth stage
- Evapotranspiration rate
AI Model Training
import tensorflow as tf
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(15,)),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(8, activation='sigmoid') # 8 zones
])
model.compile(
optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy']
)
Mobile App Integration
The BAYYTI mobile app provides complete remote control and monitoring:
Key Features
- Real-time sensor data visualization
- Manual and automatic irrigation control
- Historical data and analytics
- Water usage reports
- Alert notifications
- Multi-user access control
Installation Process
1. Physical Installation
# Mount controller in weatherproof location
# Connect solar panel (50W, 12V)
# Install battery (12V 20Ah)
# Connect 4G antenna
2. Sensor Wiring
Connect soil moisture sensors using shielded cables (max 100m distance):
Sensor Pin Layout:
- Red: VCC (3.3V)
- Black: GND
- Yellow: Signal (Analog)
3. Valve Connection
Wire solenoid valves to the controller outputs with proper grounding.
4. Software Configuration
// Configure via web interface
const config = {
zones: [
{
id: 1,
name: "Zone A - Vegetables",
sensorId: "SM001",
valveId: "V001",
cropType: "tomatoes",
minMoisture: 35,
maxMoisture: 65
}
],
schedule: {
mode: "auto",
maxDailyWatering: 120, // minutes
preferredTimes: ["06:00", "18:00"]
}
};
Performance Metrics
Real-world performance data from 50+ installations:
- Water Savings: 35-45% average reduction
- Uptime: 99.7% (including solar power)
- Response Time: < 2 seconds for manual commands
- Battery Life: 7 days without solar (full charge)
- Sensor Accuracy: ±2% moisture reading
Maintenance
Monthly Tasks
- Clean solar panel surface
- Check battery voltage
- Inspect sensor connections
- Verify valve operation
Annual Tasks
- Calibrate soil sensors
- Replace air filters
- Update firmware
- Battery health check
Troubleshooting
Common Issues
Issue: Sensor reading stuck at 0%
Solution: Check sensor connection and clean sensor probes
Issue: Valve not opening
Solution: Verify 24V AC power supply and check valve solenoid
Conclusion
The BAYYTI-B1 represents the cutting edge of smart irrigation technology, combining robust hardware, intelligent software, and AI optimization to deliver unmatched water efficiency and crop management capabilities.