{"product_id":"kit-esp32-air-quality-intelligence-node","title":"ESP32 Air Quality Intelligence Node","description":"\u003ch1\u003eESP32 Air Quality Intelligence Node: Predict AQI with On-Device AI and Stream to Node-RED\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 Beginner\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 3-4 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 15-18\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e TinyML regression with TensorFlow Lite\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eThis kit transforms air quality sensing into predictive intelligence. Instead of merely reading current gas levels, you’ll train and deploy a TensorFlow Lite regression model on the ESP32 that infers the Air Quality Index from MQ135 and DHT22 readings, then streams predictions to a live Node-RED dashboard. It’s a compact, real-world edge AI system perfect for monitoring indoor pollution at home, demonstrating smart city concepts at hackathons, or completing CBSE Class 11–12 AI\/IoT practicals.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou’ll assemble a complete air quality intelligence node. The MQ135 detects smoke, CO₂, and other harmful gases; the DHT22 captures temperature and humidity. The ESP32 runs a pre‑trained TensorFlow Lite regression model to calculate AQI instantly — no cloud processing needed. The OLED displays the index locally, while MQTT pushes live predictions to a Node-RED dashboard you can access from any phone or laptop. Everything is wired on a breadboard‑free base, housed neatly, and powered by a single MicroUSB cable.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eCollect and preprocess multi‑sensor data for edge machine learning\u003c\/li\u003e\n  \u003cli\u003eTrain a TensorFlow Lite regression model and convert it for microcontroller deployment\u003c\/li\u003e\n  \u003cli\u003eStream real‑time IoT predictions to a Node‑RED dashboard via MQTT\u003c\/li\u003e\n  \u003cli\u003eBuild an end‑to‑end edge AI pipeline — from physical sensor to cloud visualization\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\u003eESP32 Dev Board\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eMQ135 Gas Sensor\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eDHT22\u003c\/td\u003e\n\u003ctd\u003e1\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\u003eMicroUSB Cable\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\u003e15\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\u003eCBSE Class 11–12 students pursuing the AI or IoT elective will find this kit directly aligns with practical‑file requirements. B.Tech ECE\/EEE undergraduates can use it for mini‑projects or Smart India Hackathon prototypes. ATL Tinkering Lab mentors and IIT\/NIT\/VIT\/BITS workshop attendees will appreciate its structured learning path and immediate, visible outcomes — an AI model running on a device smaller than a credit card.\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 open the AI companion trained on this exact project. It offers step‑by‑step debugging, and our support team is reachable on WhatsApp for human help within hours.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWill this kit work without internet?\u003c\/summary\u003e\u003cp\u003eThe ESP32 runs the TFLite model entirely offline — AQI predictions appear on the OLED even without Wi‑Fi. Internet is only required to stream data to the Node‑RED dashboard.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I customize the prediction model?\u003c\/summary\u003e\u003cp\u003eYes. The AI companion includes a guide to collect your own air quality data, retrain the regression model on Google Colab, and deploy the updated model to the ESP32.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eIs this suitable for school science exhibitions?\u003c\/summary\u003e\u003cp\u003eAbsolutely. The beginner difficulty, clear learning outcomes, and real‑world pollution theme make it ideal for CBSE science fairs, ATL innovation marathons, and Smart India Hackathon.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eMQ135 and DHT22 data feeds a TFLite regression model on ESP32 predicting AQI — streams predictions to Node-RED.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/esp32-30-pin-development-board-cp2102-wifi-bluetooth\"\u003eESP32 Dev Board\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eMQ135 Gas Sensor\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/dht22-temperature-humidity-sensor-module-accurate-readings\"\u003eDHT22\u003c\/a\u003e\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\/microusb-cable-1m-charging-data-cord-for-arduino-android\"\u003eMicroUSB Cable\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eM-M Wires x15\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 ESP32 Air Quality Intelligence Node?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The ESP32 Air Quality Intelligence Node includes all components needed: ESP32 Dev Board, MQ135 Gas Sensor, DHT22, 0.96in OLED, MicroUSB Cable 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 ESP32 Air Quality Intelligence Node?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Beginner level makers, suitable for ages 15-18. MQ135 and DHT22 data feeds a TFLite regression model on ESP32 predicting AQI — streams predictions to Node-RED. Estimated build time is 3-4 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the ESP32 Air Quality Intelligence Node online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the ESP32 Air Quality Intelligence Node is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. 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