ECE Lab Kit 27 Smart Irrigation Controller
Smart Irrigation Controller Kit — Compare Rule-Based vs ML Scheduling on ESP32
Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.
Students assemble a multi-zone irrigation controller that reads soil moisture from three probes and ambient temperature/humidity, then uses an ESP32 to decide when to open a solenoid valve. After the physical build, they switch between two control strategies — a threshold-based rule engine and a lightweight machine learning model trained on logged data — and analyze which approach saves more water while keeping plants healthy.
What You'll Build
A fully functional smart irrigation system with three moisture sensors placed in different soil zones, real-time temperature and humidity monitoring via the DHT22, and a 12V solenoid valve activated through a relay. The ESP32 runs two selectable programs: a rule-based algorithm with user-adjustable moisture thresholds and hysteresis, and a TinyML model that learns optimal watering times from historical patterns, all displayed on a simple dashboard for comparison.
What You'll Learn
- Capturing and preprocessing multi-sensor analog/digital data on the ESP32
- Implementing a rule-based irrigation controller with hysteresis to prevent valve chattering
- Training a TinyML model using exported sensor logs and deploying it with TensorFlow Lite Micro
- Comparing water consumption and soil moisture trends between rule-based and ML-driven scheduling workflows
Kit Contents
| Component | Quantity |
|---|---|
| ESP32 Dev Board | 1 |
| Soil Moisture Sensor | 3 |
| DHT22 Temperature & Humidity Sensor | 1 |
| 5V Relay Module | 1 |
| Solenoid Valve | 1 |
| 12V Power Supply Unit | 1 |
| USB Cable | 1 |
| M-M Jumper Wires | 20 |
Why Buy This Kit Instead of Sourcing Parts Separately
| Factor | Sourcing Separately | Compoden Kit |
|---|---|---|
| Compatibility checks | You verify every part | Pre-tested as a system |
| Build support | Forums and scattered tutorials | AI companion trained on this exact project |
| Time to first working build | Days of debugging | Hours, with step-by-step guidance |
| Shipping coordination | Multiple sellers, multiple delays | One shipment from Bengaluru in 3-5 days |
Who This Kit Is For
Designed for B.Tech ECE, EEE, and CSE students tackling lab mini-projects or final-year work on IoT and smart agriculture. Perfect for CBSE Class 12 informatics practices students exploring ESP32 microcontrollers, and for teams in Smart India Hackathon designing precision farming prototypes. ATL tinkering lab mentors will value the dual‑algorithm comparison as a ready‑to‑run lesson in embedded decision systems.
Built and Backed by Compoden
Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.
What if I get stuck during the build?
Scan the QR code on the box to access the AI build companion trained on this exact project. It provides step-by-step wiring checks, code troubleshooting, and calibration guidance. WhatsApp and email support are also available as backup.
Can I really train a machine learning model on the ESP32?
Yes. The companion software includes a data logger that records sensor readings over time. You export these logs, train a TinyML model (a simple decision tree or neural network) on your laptop using the provided script, and deploy it onto the ESP32 for on-device inference.
How do I compare rule-based vs ML scheduling outcomes?
The ESP32 code base offers two selectable modes. Run the rule-based mode with adjustable moisture thresholds, then switch to ML mode where the device predicts optimal watering windows. An included dashboard script plots cumulative water usage and soil moisture curves, making the efficiency comparison quantitative.
Do I need prior machine learning experience?
No. The AI companion walks you through the entire TinyML pipeline — from data logging to model training to deployment — using beginner-friendly tools. Basic familiarity with the Arduino IDE is enough to get started.
Soil moisture and temperature on ESP32 decide when to water — compares rule-based vs ML-based scheduling.
What's in this kit
- ESP32 Dev Board
- Soil Moisture Sensor x3
- DHT22
- 5V Relay Module
- Solenoid Valve
- 12V PSU
- USB Cable
- M-M Wires x20
Ask Soldr above what you can build with this — it knows every Compoden kit this part appears in.
Shipping Information
- Prepaid Orders: ₹75 for orders up to ₹999, FREE shipping above ₹999
- COD Orders: ₹125 shipping + ₹50 COD fee = ₹175 total
- Delivery Timeline: Dispatch in 1-2 days, delivery in 2-7 days depending on location
Returns & Warranty
- 7-Day Return: Manufacturing defects only (approval required)
- Warranty: 7 days from delivery
- Non-Returnable: Batteries, consumables, cut wires, clearance items