Raspberry Pi 5 Plant Disease Detection Kit - AI Camera Classifies Leaf Diseases
Raspberry Pi 5 Plant Disease Detection Kit — Build an AI Camera That Identifies Leaf Diseases
Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.
Plant nurseries, agricultural retail outlets, and field scouts all face the same challenge — quickly identifying leaf diseases before they spread. With this kit you’ll build a handheld AI camera that uses a TensorFlow Lite model trained on the PlantVillage dataset to classify leaf diseases from a Raspberry Pi camera image. Point the camera at a leaf, snap a picture, and within milliseconds the OLED shows the most likely disease and its confidence score. The entire inference pipeline runs offline on the Raspberry Pi, so you can carry it into any greenhouse, polyhouse, or retail plant aisle without worrying about Wi‑Fi.
What You'll Build
You’ll assemble a self-contained plant disease diagnosis tool: a Raspberry Pi 5 with a Pi Camera Module 3 captures high‑resolution leaf images, stores the TFLite model and data on a blazing‑fast NVMe SSD attached via the M.2 HAT+, and displays results on a crisp 0.96‑inch OLED. The final device fits in one hand and helps you answer “Is this leaf healthy, or does it show signs of bacterial spot, late blight, or rust?” with the press of a button.
What You'll Learn
- Setting up a Raspberry Pi 5 with an NVMe SSD as the primary drive
- Connecting and configuring the Pi Camera Module 3 for still capture
- Running a quantized TensorFlow Lite model on‑device with hardware acceleration
- Interfacing a 0.96‑inch OLED display over I2C to show inference results
Kit Contents
| Component | Quantity |
|---|---|
| Raspberry Pi 5 4GB | 1 |
| Pi Camera Module 3 | 1 |
| NVMe SSD 128GB | 1 |
| Pi 5 M.2 HAT+ | 1 |
| 0.96in OLED | 1 |
| USB-C PSU | 1 |
| M-M Wires | 10 |
Why Buy This Kit Instead of Sourcing Parts Separately
| Factor | Sourcing Separately | Compoden Kit |
|---|---|---|
| Compatibility checks | You verify every part | Pre-tested as a system |
| Build support | Forums and scattered tutorials | AI companion trained on this exact project |
| Time to first working build | Days of debugging | Hours, with step-by-step guidance |
| Shipping coordination | Multiple sellers, multiple delays | One shipment from Bengaluru in 3-5 days |
Who This Kit Is For
This kit is built for intermediate makers who have some Python experience and want to dive into edge AI. Students working on agri‑tech projects for CBSE Class 11–12, B.Tech ECE/EEE, Smart India Hackathon teams, or ATL Tinkering Labs will find it a practical way to apply computer vision to a real‑world problem. If you’re a plant nursery owner who likes to tinker or a field researcher prototyping a disease monitoring tool, the self‑contained offline operation makes it an ideal starting point.
Built and Backed by Compoden
Every 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.
What if I get stuck during the build?
Scan the QR code included in the box to access the AI companion trained on this kit; it can walk you through every step, from OS installation on the NVMe SSD to wiring the OLED. If you need a human touch, WhatsApp support is also available.
Can I train the model on my own dataset of leaf diseases?
Yes, the NVMe SSD provides ample space and speed for storing a custom dataset. The AI companion includes guidance on retraining the TFLite model with your own images, so you can extend it to local crop diseases beyond the PlantVillage classes.
Is this kit suitable for use in a polyhouse with no internet?
Absolutely — the TensorFlow Lite inference runs entirely offline on the Pi. Once the model is loaded onto the SSD, you can diagnose leaves anywhere, no Wi‑Fi or mobile network needed.
What if a component fails during the build?
We replace any part that has a manufacturing defect within 7 days of delivery. Just reach out via the AI companion or WhatsApp, and we’ll ship a replacement immediately.
Retail Analytics — TFLite plant disease classifier trained on PlantVillage dataset identifies leaf diseases from Pi camera images.
What's in this kit
Shipping Information
- Prepaid Orders: ₹75 for orders up to ₹999, FREE shipping above ₹999
- COD Orders: ₹125 shipping + ₹50 COD fee = ₹175 total
- Delivery Timeline: Dispatch in 1-2 days, delivery in 2-7 days depending on location
Returns & Warranty
- 7-Day Return: Manufacturing defects only (approval required)
- Warranty: 7 days from delivery
- Non-Returnable: Batteries, consumables, cut wires, clearance items