AI LSTM on Raspberry Pi 5 Predicts Irrigation Demand 48h Ahead

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

Train a Long Short-Term Memory (LSTM) neural network on your Raspberry Pi 5 using real weather patterns and multi-point soil moisture data. The result: a smart irrigation predictor that tells you exactly when and how much to water - 48 hours in advance, cutting waste while keeping plants healthy.

What You'll Build

A fully functional edge-AI system that runs a trained LSTM model on a Raspberry Pi 5, pulling in data from two DHT22 temperature/humidity sensors, a BMP280 pressure sensor, and three soil moisture probes. The system logs historical trends to an NVMe SSD, periodically retrains the model, and outputs a 48-hour irrigation schedule displayed via a local web dashboard.

What You'll Learn

• Collecting and structuring environmental time-series data from multiple sensors
• Training and deploying an LSTM model on a Raspberry Pi 5 using TensorFlow Lite
• Integrating I2C and analog sensors (soil moisture, DHT22, BMP280) with edge hardware
• Automating model retraining and prediction pipelines with Python and cron jobs

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

Designed for B.Tech ECE/EEE students building edge-AI prototypes, Smart India Hackathon teams working on AgriTech challenges, and ATL Tinkering Lab mentors teaching IoT + ML integration. Also suitable for CBSE Class 12 students exploring advanced sensor networks and anyone over 15 keen on applying AI in gardening.

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