ESP32 Greenhouse Soil Manager Kit – Automate Watering & Compare Rule-Based vs ML Scheduling

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

Create a smart greenhouse irrigation controller that goes far beyond a simple timer. This kit places you at the intersection of IoT and machine learning: you’ll wire up three soil moisture sensors and a DHT22 to an ESP32, then program both a traditional rule-based watering logic and a machine learning model that predicts the optimal irrigation moment. The hardware actuates a real solenoid valve, letting you physically see which approach saves more water and keeps soil conditions ideal.

What You'll Build

A sensor network that monitors soil moisture at multiple depths and tracks ambient temperature/humidity with the DHT22. The ESP32 processes this data and toggles a 5V relay to open or close the solenoid valve, controlling a water line. You’ll implement a threshold-based rule engine (e.g., water when moisture drops below 30%) and contrast it against a tiny ML model trained on simulated historical patterns. The kit turns abstract concepts into a hands-on comparison you can demo and measure.

What You'll Learn

• Calibrate capacitive soil moisture sensors and interpret analog readings on the ESP32
• Design multi-threshold rule-based irrigation with adjustable moisture and temperature triggers
• Collect offline sensor data and train a basic decision-tree model for irrigation prediction
• Deploy the model on ESP32 using TensorFlow Lite Micro and measure water savings against the rule-based approach

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

Built for B.Tech (ECE, EEE, CS, Agriculture Engineering) and polytechnic students diving into precision agriculture. Perfect for Smart India Hackathon teams prototyping agri-tech solutions, CBSE/CISCE Class 11–12 students pursuing AI/ML electives, and ATL Tinkering Labs that want a tangible IoT-meets-ML experience. It demands intermediate Arduino/ESP32 familiarity but walks you through the ML part step by step.

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.