🌱Smart Plant Monitoring & Automatic Irrigation System
Group A10 | 03.12.2026
This project implements a Smart Plant Monitoring System using Arduino UNO to monitor temperature, humidity, soil moisture, and water-tank level, automatically activating a 5V water pump when soil moisture is low. Visual/audible alerts (LCD, LEDs, buzzer) and serial transmission to a Python dashboard provide real-time monitoring.
Objectives
Continuous Soil Monitoring
Continuously measure soil moisture and trigger irrigation when thresholds are crossed.
Automatic Irrigation
Control a water pump automatically based on soil moisture readings (pump ON/OFF).
Environmental Sensing
Measure temperature and humidity with DHT11 for context-aware monitoring.
Tank Level Detection & Alerts
Use ultrasonic sensor to detect water level; generate buzzer alerts when tank is nearly empty.
Feedback & Telemetry
Provide LCD, LEDs, buzzer feedback and stream data to a Python dashboard for visualization.
System Overview - Process Flow
The system: sensors → Arduino → decision logic for irrigation and alerts → serial transmission to a Python dashboard for live graphs and status. This ensures automated watering, visual/audible feedback, and remote monitoring via plots.
Hardware Components
Arduino UNO
Main microcontroller controlling sensors, logic, and actuator outputs.
DHT11 Sensor
Measures ambient temperature and humidity (example LCD: T:28 H:70).
Soil Moisture Sensor
Reports soil water content as a percentage (example LCD: Soil:45).
Ultrasonic Sensor (HC-SR04)
Measures water-tank distance; triggers buzzer if distance >15 cm (tank nearly empty).
16x2 LCD (I2C)
Displays status messages such as 'Watering' or 'Good'.
Water Pump (5V)
Activated to irrigate plants when soil moisture < 30%.
Pin Configuration & Circuit Summary
Pin Assignments
- D2 — DHT11 Data
- A0 — Soil Moisture
- D7 — Water Pump
- D8 — Buzzer
- D9 — Ultrasonic TRIG
- D10 — Ultrasonic ECHO
- D6 — Red LED
- D5 — Green LED
- A4/A5 — LCD SDA/SCL
Key Connections
Use 5V and GND rails for sensors and actuators. Connect a 10kΩ pull-up between VCC and DATA for DHT11. Use 2N2222 transistor to drive pump and 220Ω resistors for LEDs and buzzer.
Power: 9V battery supplies the system; verify current capability for the pump or use external power if needed.
System Functions & Behavior
01
Temperature & Humidity
DHT11 measures environmental T and H; sample LCD output: T:28 H:70. Data is sent to the Python dashboard for plotting.
02
Soil Moisture Monitoring
Soil sensor outputs percentage; if Soil < 30% the system activates pump and shows 'Watering' on LCD; Red LED ON.
03
Water Level Detection
Ultrasonic sensor measures tank distance; if distance >15 cm buzzer sounds to warn about low water.
04
Visual & Audible Feedback
LCD displays status; Red LED indicates watering; Green LED indicates sufficient moisture; buzzer alerts for empty tank.
Serial Format & Python Dashboard
Arduino transmits CSV-formatted data over serial: Temperature,Humidity,SoilMoisture,WaterLevel. Example: 28,70,45,12. A Python dashboard (NumPy + Matplotlib) reads serial data and renders four live plots: Temperature, Humidity, Soil Moisture, and Tank Level, plus a top status bar showing current readings.
Figure captions: Dashboards showing 'SOIL MOISTURE GOOD' and 'WATERING' states with real-time graphs and status indicators.
Advantages & Limitations
Advantages
- Automatic irrigation conserves water
- Continuous real-time monitoring
- Early warning for empty tank
- Low-cost smart agriculture solution
Limitations
- Ultrasonic readings can be noisy due to reflections
- Irrigation logic uses a fixed threshold (not adaptive)
- No cloud-based remote access in current design
- Plant health monitoring lacks visual inspection (no camera)
AI Integration - Opportunities & Use Cases
Predictive Irrigation
Use regression/trend analysis on soil moisture, temperature, and humidity to predict drying rate and trigger proactive watering before critical levels are reached—optimizes water usage and prevents stress.
Plant Health Detection
Integrate a camera and CNN-based vision to classify leaf health (Healthy / Stressed / Diseased) and surface alerts on the dashboard for early intervention.
Water Consumption Optimization
Train models on historical irrigation and conditions to predict optimal pump duration dynamically—reduces waste and improves irrigation efficiency.
AI-Driven Insights
Analyze historical patterns to surface recommendations and anomaly detection (e.g., 'Soil moisture decreasing rapidly').
Architecture: Sensors → Arduino → Serial → Python Processing → AI Model → Decisions → Dashboard. AI can transform threshold-based control into adaptive, predictive irrigation with visual health analytics.
Conclusion, Future Work & Acknowledgment
Conclusion
The project demonstrates an integrated Arduino + Python solution for automatic irrigation, real-time visualization, and alerts—combining embedded systems and data visualization into a practical smart agriculture tool.
Future Work
Planned improvements: IoT/cloud connectivity, mobile app integration, multi-plant support, solar power, automated tank refill, camera integration, and AI models for predictive irrigation and plant health.
Acknowledgment
Thanks to course instructors and evaluators for guidance. AI tools assisted in ideation and documentation but required careful verification and hands-on testing for hardware-specific details.
This Smart Plant Monitoring System successfully integrates Arduino hardware, sensor data processing, and Python visualization to deliver an automated, real-time irrigation solution that demonstrates practical embedded systems engineering with potential for AI-driven optimization.
Team Members CO1010 / Group - A10
THANK YOU!
"The future of agriculture is not just growing plants - it’s growing intelligence around them."