{"product_id":"kit-pi-5-predictive-crop-yield-model","title":"Pi 5 Predictive Crop Yield Model","description":"\u003ch1\u003eRaspberry Pi 5 Predictive Crop Yield Model Kit with IoT Sensors \u0026amp; LSTM\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 Advanced\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 8-10 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 18-25\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Time Series ML \u0026amp; IoT Data Collection\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eImagine deploying a Raspberry Pi in a field that not only logs temperature, humidity, soil moisture, and light levels but also learns from past seasons to forecast tomorrow's harvest. This kit transforms that scenario into a working edge AI system—no cloud dependency, no theoretical textbook exercise. You wire real sensors to a Pi 5, stream data onto NVMe storage, and train an LSTM neural network that predicts crop yield from historical patterns. It’s precision agriculture built from first principles.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou assemble a complete edge intelligence station: a Raspberry Pi 5 with DHT22, capacitive soil moisture probe, and BH1750 lux sensor collecting environmental data at regular intervals. Raw readings are logged to the 512GB NVMe SSD via the M.2 HAT, creating a rich time series. Using TensorFlow or PyTorch, you train a multi-layer LSTM on this dataset to output a yield forecast—ready for validation against real harvest figures.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eTrain an LSTM network on multivariate time series data from agricultural sensors\u003c\/li\u003e\n  \u003cli\u003eBuild an end-to-end IoT data pipeline on Raspberry Pi 5 with NVMe SSD persistence\u003c\/li\u003e\n  \u003cli\u003eCalibrate DHT22, soil moisture, and light sensors for reliable long-term outdoor logging\u003c\/li\u003e\n  \u003cli\u003eDeploy a lightweight inference engine locally—no cloud, no latency, just edge AI\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 8GB\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\u003eSoil Moisture Sensor\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBH1750 Light Sensor\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNVMe SSD 512GB\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\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 meets the needs of advanced BTech ECE, EEE, and agricultural engineering students working on final-year capstone projects at IITs, NITs, VIT, or BITS. It’s equally suited for Smart India Hackathon teams tackling precision agriculture challenges, ATL Tinkering Lab mentors running IoT+AI workshops, and independent makers who want a real-world time series ML experience without sourcing sensors one by one.\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 to launch the AI companion trained on this exact crop yield predictor; it will walk you through wiring, coding, and model training. You can also reach us over WhatsApp for human assistance within 24 hours.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhere can I source the historical sensor data to train the LSTM?\u003c\/summary\u003e\u003cp\u003eThe AI companion provides a sample dataset of environmental readings with corresponding yield labels so you can start training immediately. You can later replace it with your own collected sensor logs.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDo I need prior deep learning experience to complete this project?\u003c\/summary\u003e\u003cp\u003eFamiliarity with Python and basic neural networks helps, but the companion guides you through setting up TensorFlow, defining the LSTM architecture, and tuning hyperparameters—turning it into a learn-by-building experience.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan this system run on battery power on a remote farm?\u003c\/summary\u003e\u003cp\u003eYes, the Pi 5 with sensors draws under 15W and can run off a solar-charged power bank. The kit includes a stable USB-C PSU for bench development, but field deployment with alternative power is straightforward.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eHistorical sensor data trains an LSTM on Pi 5 to predict crop yield — combines IoT data collection with time series ML.\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-8gb-high-performance-single-board-computer\"\u003eRaspberry Pi 5 8GB\u003c\/a\u003e\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\/soil-moisture-sensor-module-analog-digital-output-compoden\"\u003eSoil Moisture Sensor\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/gy-30-bh1750fvi-light-intensity-sensor-module-for-arduinoesp\"\u003eBH1750 Light Sensor\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 512GB\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 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 Pi 5 Predictive Crop Yield Model?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Predictive Crop Yield Model includes all components needed: Raspberry Pi 5 8GB, DHT22, Soil Moisture Sensor, BH1750 Light Sensor, NVMe SSD 512GB 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 Predictive Crop Yield Model?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Advanced level makers, suitable for ages 18-25. Historical sensor data trains an LSTM on Pi 5 to predict crop yield — combines IoT data collection with time series ML. Estimated build time is 8-10 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Predictive Crop Yield Model online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Predictive Crop Yield Model 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\": \"Pi 5 Predictive Crop Yield Model\",\n  \"description\": \"Historical sensor data trains an LSTM on Pi 5 to predict crop yield — combines IoT data collection with time series ML.\",\n  \"sku\": \"CDN-KIT-2586\",\n  \"brand\": {\"@type\": \"Brand\", \"name\": \"Compoden\"},\n  \"offers\": {\n    \"@type\": \"Offer\",\n    \"url\": \"https:\/\/compoden.in\/products\/kit-pi-5-predictive-crop-yield-model\",\n    \"priceCurrency\": \"INR\",\n    \"price\": \"51040\",\n    \"availability\": \"https:\/\/schema.org\/InStock\",\n    \"seller\": {\"@type\": \"Organization\", \"name\": \"Compoden\"}\n  },\n  \"category\": \"Edge AI \u0026 Computer Vision\"\n}\n\u003c\/script\u003e","brand":"Compoden","offers":[{"title":"Default Title","offer_id":53469371105645,"sku":"CDN-KIT-2586","price":60230.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0999\/3997\/5533\/files\/kit-pi-5-predictive-crop-yield-model.png?v=1781948445","url":"https:\/\/compoden.com\/products\/kit-pi-5-predictive-crop-yield-model","provider":"Compoden","version":"1.0","type":"link"}