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Farm Automation ​

DifficultyAdvanced
Team Size2-3 people
Time~25-30 hours
Demo-ready byStep 4
PrerequisitesPython, Raspberry Pi, basic electronics, sensor basics
Built byFarmBot, CropX, Agrilyst, Netafim

Skills you'll earn: Sensor interfacing (I2C/SPI), relay control, MQTT IoT, scheduling systems, dashboard visualization

Start with reading a sensor. End with an automated irrigation system.

Step 1: Read a soil moisture sensor ​

You want to know if the soil is wet or dry.

  • Set up a Raspberry Pi or Arduino with a capacitive soil moisture sensor
  • Read the analog value (use an ADC like MCP3008 on the Pi, or Arduino's analog pins)
  • Print the raw value to the console
  • Map the raw value to a percentage: 0% = bone dry, 100% = saturated

You now have: A soil moisture reading.

Step 2: Log readings over time ​

A single reading tells you nothing about trends.

  • Read the sensor every 5 minutes
  • Log each reading with a timestamp to a CSV file or SQLite database
  • Plot the data: time on X axis, moisture on Y axis
  • See how moisture changes through the day

You now have: Historical moisture data.

Step 3: Add more sensors ​

Moisture alone isn't the full picture.

  • Add a DHT22 sensor for temperature and humidity
  • Add a light sensor (LDR or BH1750)
  • Read all sensors in the same loop
  • Log all readings together

You now have: Multi-sensor environmental monitoring.

Step 4: Build a dashboard ​

Looking at CSV files isn't practical in the field.

  • Set up a Flask or FastAPI server on the Pi
  • Serve a web page with real-time sensor readings
  • Use Chart.js to show historical graphs
  • Auto-refresh or use WebSocket for live updates

You now have: A monitoring dashboard.

Step 5: Automate irrigation ​

You check the dashboard and manually water. The point is to not do that.

  • Connect a relay module to a water pump or solenoid valve
  • Define a rule: if soil moisture drops below 30%, turn on the pump
  • Turn off when moisture reaches 60%
  • Add a minimum interval between waterings to prevent overwatering

You now have: Automated irrigation.

Step 6: Scheduling and rules engine ​

Simple thresholds aren't always right. Morning watering is better than noon watering.

  • Add time-based rules: only water between 6 AM and 8 AM
  • Combine conditions: moisture < 30% AND time is 6-8 AM AND temperature < 35°C
  • Make rules configurable from the dashboard
  • Log every irrigation event with the triggering conditions

You now have: A rules-based automation system.

Step 7: Alerts ​

The pump failed. The soil is at 10% moisture and no one knows.

  • Send alerts when conditions are critical: moisture too low, temperature too high, pump not responding
  • Use email, SMS (via Twilio), or a messaging webhook
  • Alert on sensor failures (NaN readings, no data)

Step 8: Weather integration ​

  • Fetch weather forecasts from Open-Meteo
  • Skip irrigation if rain is expected in the next 12 hours
  • Adjust watering intensity based on temperature forecast

Step 9: Multi-zone support ​

  • Support multiple sensor/valve zones (each bed or field section)
  • Independent rules per zone
  • Stagger irrigation to manage water pressure

Useful Resources ​

Where to go from here ​

  • Camera-based crop monitoring (disease detection, growth tracking)
  • Solar-powered setup for remote fields
  • LoRa or cellular connectivity for fields without WiFi
  • Machine learning: predict optimal watering from historical data
  • Integration with agricultural management software