{"product_id":"kit-pi-5-cold-chain-temperature-monitor","title":"Pi 5 Cold Chain Temperature Monitor","description":"\u003ch1\u003eRaspberry Pi 5 Cold Chain Temperature Monitor — LSTM-Powered Anomaly Detection for Refrigerated Logistics\u003c\/h1\u003e\n\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Intermediate\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 4-5 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 15-21\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e AI Model Deployment on Edge\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eImagine 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.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou 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.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eInterface four DS18B20 one‑wire sensors on Raspberry Pi 5 and pull synchronous temperature data every second.\u003c\/li\u003e\n  \u003cli\u003eSet up NVMe SSD as the Pi’s boot drive using the M.2 HAT+ for fast, reliable data logging.\u003c\/li\u003e\n  \u003cli\u003eTrain a compact LSTM neural network on time-series temperature data to model a cooling curve and run inference on the edge.\u003c\/li\u003e\n  \u003cli\u003eBuild a real-time anomaly detection pipeline that compares live sensor values against the model’s prediction and triggers an OLED-based alarm.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRaspberry Pi 5 4GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eDS18B20 Waterproof\u003c\/td\u003e\n\u003ctd\u003e4\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e0.96in OLED\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNVMe SSD 128GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi 5 M.2 HAT+\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eM-M Wires\u003c\/td\u003e\n\u003ctd\u003e20\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eB.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.\u003c\/p\u003e\n\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery 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.\u003c\/p\u003e\n\n\u003cdetails\u003e\u003csummary\u003eWhat if I get stuck during the build?\u003c\/summary\u003e\u003cp\u003eScan the QR code inside the box to start the AI companion, which has been trained on this exact kit and its build steps. If anything remains unclear, WhatsApp us directly — a real human from our Bengaluru team will walk you through it.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I use this for my final year engineering project?\u003c\/summary\u003e\u003cp\u003eAbsolutely. The kit produces a working cold chain monitor with live anomaly detection. You’ll have a robust hardware-and-AI demo, plus full access to the model training pipeline to adapt it for your project report or viva.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDoes the AI model need internet to detect anomalies?\u003c\/summary\u003e\u003cp\u003eNo. The LSTM model is trained offline and runs directly on the Raspberry Pi 5. All inference happens locally, so the system works inside a refrigerated container even without any network connectivity.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow accurate is the anomaly detection?\u003c\/summary\u003e\u003cp\u003eThe LSTM model learns the expected cooling curve from your own logged data. After training, it reliably flags deviations beyond a configurable threshold — catching compressor failures or door-open events within seconds. You can fine-tune sensitivity easily.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eDS18B20 probes in refrigerated container log temperature — LSTM detects deviation from expected cooling curve and alerts.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-model-b-4gb-technical-specs-projects\"\u003eRaspberry Pi 5 4GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/ds18b20-waterproof-temperature-sensor-probe-55c-to-125c\"\u003eDS18B20 Waterproof\u003c\/a\u003e x4\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/096in-oled-display-128x64-i2cspi-for-arduino-raspberry-pi\"\u003e0.96in OLED\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/official-raspberry-pi-m2-hat-nvme-ssd-add-on-board-for-pi-5\"\u003eNVMe SSD 128GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-pcie-to-m2-nvme-ssd-expansion-board-by-elecrow\"\u003ePi 5 M.2 HAT+\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-4-official-power-supply-5v-3a-usb-c-compoden\"\u003eUSB-C PSU\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eM-M Wires x20\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Pi 5 Cold Chain Temperature Monitor?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Cold Chain Temperature Monitor includes all components needed: Raspberry Pi 5 4GB, DS18B20 Waterproof, 0.96in OLED, NVMe SSD 128GB, Pi 5 M.2 HAT+ and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Pi 5 Cold Chain Temperature Monitor?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Intermediate level makers, suitable for ages 15-21. DS18B20 probes in refrigerated container log temperature — LSTM detects deviation from expected cooling curve and alerts. Estimated build time is 4-5 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Cold Chain Temperature Monitor online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Cold Chain Temperature Monitor is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. 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