Raspberry Pi 5 Cold Chain Temperature Monitor — LSTM-Powered Anomaly Detection for Refrigerated Logistics

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

Imagine a refrigerated truck carrying vaccines or perishable foods — inside, four waterproof sensors track temperature minute by minute. A Raspberry Pi 5 runs a local LSTM model that has learned the expected cooling curve of the container. The moment the real temperature trace deviates from what the model predicts, an alert fires. That’s exactly what you’ll build with this kit, bringing machine learning out of the cloud and into a real-world cold chain monitoring system.

What You'll Build

You will assemble a fully functional temperature logging station that mounts inside a cold storage unit. Four DS18B20 probes read temperatures at different points inside the container. A 0.96-inch OLED shows live readings, while all data is stored on a high-speed NVMe SSD. The onboard LSTM model, trained to recognise normal cooling behaviour, detects anomalies like door left open, compressor failure, or power loss — and triggers a local alert. The entire system runs autonomously, no internet required for detection.

What You'll Learn

• Interface four DS18B20 one‑wire sensors on Raspberry Pi 5 and pull synchronous temperature data every second.
• Set up NVMe SSD as the Pi’s boot drive using the M.2 HAT+ for fast, reliable data logging.
• Train a compact LSTM neural network on time-series temperature data to model a cooling curve and run inference on the edge.
• Build a real-time anomaly detection pipeline that compares live sensor values against the model’s prediction and triggers an OLED-based alarm.

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

B.Tech ECE/EEE students working on IoT or AI-based cold chain projects will find a complete edge-AI pipeline ready to deploy. CBSE Class 11-12 and ATL Tinkering Lab teams can use this as a capstone project that combines sensor integration with data science. Smart India Hackathon participants and IIT/NIT/VIT/BITS project groups get a ready-made hardware platform to iterate on cold storage monitoring for pharma or food logistics in India.

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.