{"product_id":"kit-pi-5-insect-and-pest-identifier-kit","title":"Pi 5 Insect and Pest Identifier Kit","description":"\u003ch1\u003eBuild a Field‑Ready Insect Identifier with Raspberry Pi 5 — Classify 50 Indian Agricultural Pests in Real Time\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 hours\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 16-21\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Deploying TFLite on Raspberry Pi for real-time classification\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eWalk through a cotton, rice, or chilli field, snap a macro photo of an insect, and know within a second whether it’s a friend or a pest that can destroy a harvest. This kit puts an on‑device TensorFlow Lite classifier on a Pi 5, trained to recognise 50 of the most common crop‑damaging insects found across Indian farms — from pink bollworm to brown planthopper. No cloud, no latency, no recurring costs.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou’ll assemble a compact, battery‑friendly imaging device that runs a pre‑optimised TFLite model entirely offline. The Pi Camera Module 3 with macro lens and ring light captures sharp, evenly lit photos of insects as small as 2 mm. Inference happens on the Raspberry Pi 5’s quad‑core Arm processor, delivering a pest label and confidence score on a local display. The result is a portable, field‑ready crop scout that works where internet cannot reach — exactly what Indian agriculture needs.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eConfigure a Raspberry Pi 5 with NVMe SSD over M.2 HAT+ for high‑speed model loading and image caching\u003c\/li\u003e\n  \u003cli\u003eDeploy a quantised TensorFlow Lite image classifier and benchmark its accuracy on real insect photos\u003c\/li\u003e\n  \u003cli\u003eCalibrate a macro lens and ring light to capture sharp, shadow‑free macro shots of moving insects\u003c\/li\u003e\n  \u003cli\u003eInterpret classification confidence thresholds and handle ambiguous identifications in a field setting\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\u003ePi Camera Module 3\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eMacro Lens Attachment\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRing Light\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  \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 is built for B.Tech agricultural engineering and ECE students designing smart‑farming tools, for CBSE Class 12 AI enthusiasts tackling practical computer vision problems, and for Smart India Hackathon teams building real‑time crop health monitors. It’s equally relevant in IIT, NIT, and VIT labs where edge AI meets agri‑tech, and fits perfectly into ATL Tinkering Lab projects that move from concept to field‑tested prototype.\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\u003eOpen the AI companion via the QR code — it has step‑by‑step guidance for this kit. If you need more help, our WhatsApp support responds within hours with tips from engineers who’ve built the same project.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDoes the insect identification work without an internet connection?\u003c\/summary\u003e\u003cp\u003eYes. The TFLite model runs entirely on the Pi 5’s processor. Once you’ve loaded the model from the SSD, you can identify pests anywhere — in a maize field, a polyhouse, or a remote orchard — without ever needing a data signal.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I retrain the model to recognise pests specific to my region?\u003c\/summary\u003e\u003cp\u003eAbsolutely. The kit comes with open‑source scripts that let you re‑train the classifier on your own insect images. Add local species like mango hoppers or tea mosquito bugs and deploy the updated model back on the Pi 5 in just a few steps.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eIs the macro lens enough to capture small insects like thrips or whiteflies?\u003c\/summary\u003e\u003cp\u003eThe macro lens attachment combined with the ring light lets you focus down to about 1 cm working distance, revealing details on insects as tiny as 1–2 mm. For thrips, you may need to hold the camera steady for a moment, but the ring light eliminates motion blur effectively.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eTFLite insect classifier on Pi 5 identifies 50 common Indian agricultural pests from macro camera images.\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\u003ca href=\"\/products\/4-channel-relay-board-for-esp32-30-pin-5v-control\"\u003ePi Camera Module 3\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eMacro Lens Attachment\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/winsen-mh-z19e-ndir-co2-sensor-module-for-air-quality-monitoring\"\u003eRing Light\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  \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 Insect and Pest Identifier Kit?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Insect and Pest Identifier Kit includes all components needed: Raspberry Pi 5 4GB, Pi Camera Module 3, Macro Lens Attachment, Ring Light, NVMe SSD 128GB 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 Insect and Pest Identifier Kit?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Intermediate level makers, suitable for ages 16-21. TFLite insect classifier on Pi 5 identifies 50 common Indian agricultural pests from macro camera images. Estimated build time is 4-5 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Insect and Pest Identifier Kit online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Insect and Pest Identifier Kit is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. 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