{"product_id":"kit-greenhouse-pressure-monitor-kit-v5","title":"Greenhouse Pressure Monitor Kit v5","description":"\u003ch1\u003eGreenhouse Pressure Monitor Kit — AI-Powered Leak Prediction with ESP32\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 Edge AI \u0026amp; Sensor Fusion\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eYour greenhouse loses pressure gradually before a leak becomes visible to the eye. This kit teaches you to catch that invisible drop the moment it starts. By pairing a flow sensor and a pressure sensor with an ESP32, you will build a device that reads the subtle relationship between flow and pressure, runs a trained TensorFlow Lite model right on the microcontroller, and sends an MQTT alert when the probability of a leak crosses a defined threshold. Instead of reacting to plant stress, you predict the risk and act first.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou will assemble a self-contained monitoring station that screws into a standard greenhouse irrigation or misting line. The OLED displays live flow rate, internal pressure, and a continuously updating leak probability score. When the TFLite model on the ESP32 determines the sensor pattern matches a pre-leak signature, the display flashes a warning and the board publishes an MQTT message over Wi-Fi. You can receive that alert on your phone via any MQTT client, giving you a real-time early warning system for pressure integrity.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eFuse flow and pressure sensor data into a single feature vector for machine learning inference\u003c\/li\u003e\n  \u003cli\u003eConvert a trained leak prediction model to TensorFlow Lite and deploy it on an ESP32 microcontroller\u003c\/li\u003e\n  \u003cli\u003eConfigure MQTT communication to send real-time alerts from an embedded device to a subscriber client\u003c\/li\u003e\n  \u003cli\u003eRender multi-variable sensor readings and inference results on a 0.96-inch OLED display\u003c\/li\u003e\n  \u003cli\u003eCalibrate sensor thresholds using live greenhouse data to distinguish normal fluctuation from fault signatures\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\u003eYF-S201 Flow Sensor\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBMP280 Pressure\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\u003eThis kit fits CBSE Class 11-12 students exploring AI-integrated physics projects and B.Tech ECE\/EEE undergraduates building edge computing prototypes. If your ATL Tinkering Lab needs a working demo that combines IoT sensor fusion with on-device machine learning, or your Smart India Hackathon team is tackling precision agriculture, the sensor pipeline and TFLite inference flow map directly to those use cases. The learning curve starts at wiring sensors and ends at deploying a trained model, making it accessible even if you have never worked with AI on a microcontroller before.\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 on the box to open the AI build companion trained on this exact kit. It will walk you through wiring, code upload, and model deployment step by step. You can also message us on WhatsApp for direct troubleshooting.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDo I need a real greenhouse to test the system?\u003c\/summary\u003e\u003cp\u003eNo. You can simulate pressure changes by gently blowing into the flow sensor inlet or partially covering it to create pressure differentials. The model will learn to associate specific flow-pressure combinations with leak signatures, and you can test the alert threshold on your desk.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eIs soldering required?\u003c\/summary\u003e\u003cp\u003eNo soldering. The kit uses male-to-male jumper wires and breadboard-compatible breakout boards. You plug the sensors directly into the ESP32, making assembly and disassembly clean and repeatable.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I modify the TFLite model to detect other anomalies?\u003c\/summary\u003e\u003cp\u003eYes. The AI companion includes a notebook that shows you how the leak probability model was trained. You can collect your own greenhouse data, retrain the model with a different fault signature like clogged drippers or pump cavitation, and redeploy the updated model to the ESP32 using the same workflow.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eGreenhouse — Flow sensor and pressure sensor data on ESP32 predicts leak probability using trained TFLite model — MQTT alert on high probability.\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\u003e\u003ca href=\"\/products\/yf-s201-water-flow-sensor-1-30lmin-hall-effect-compoden-0371\"\u003eYF-S201 Flow Sensor\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/bmp280-barometric-pressure-temperature-sensor-module-compodenin\"\u003eBMP280 Pressure\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 Greenhouse Pressure Monitor Kit v5?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Greenhouse Pressure Monitor Kit v5 includes all components needed: ESP32 Dev Board, YF-S201 Flow Sensor, BMP280 Pressure, 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 Greenhouse Pressure Monitor Kit v5?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Beginner level makers, suitable for ages 15-18. Greenhouse — Flow sensor and pressure sensor data on ESP32 predicts leak probability using trained TFLite model — MQTT alert on high probability. Estimated build time is 3-4 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Greenhouse Pressure Monitor Kit v5 online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Greenhouse Pressure Monitor Kit v5 is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. Ships from Bengaluru in 1-5 business days across India.\"\n      }\n    }\n  ]\n}\n\u003c\/script\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"Product\",\n  \"name\": \"Greenhouse Pressure Monitor Kit v5\",\n  \"description\": \"Greenhouse — Flow sensor and pressure sensor data on ESP32 predicts leak probability using trained TFLite model — MQTT alert on high probability.\",\n  \"sku\": \"CDN-KIT-4002\",\n  \"brand\": {\"@type\": \"Brand\", \"name\": \"Compoden\"},\n  \"offers\": {\n    \"@type\": \"Offer\",\n    \"url\": \"https:\/\/compoden.in\/products\/kit-greenhouse-pressure-monitor-kit-v5\",\n    \"priceCurrency\": \"INR\",\n    \"price\": \"1405\",\n    \"availability\": \"https:\/\/schema.org\/InStock\",\n    \"seller\": {\"@type\": \"Organization\", \"name\": \"Compoden\"}\n  },\n  \"category\": \"AI IoT\"\n}\n\u003c\/script\u003e","brand":"Compoden","offers":[{"title":"Default Title","offer_id":53463941087597,"sku":"CDN-KIT-4002","price":1660.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0999\/3997\/5533\/files\/kit-greenhouse-pressure-monitor-kit-v5.png?v=1781949724","url":"https:\/\/compoden.com\/products\/kit-greenhouse-pressure-monitor-kit-v5","provider":"Compoden","version":"1.0","type":"link"}