Pi 5 Food Freshness IoT Predictor
Pi 5 Food Freshness IoT Predictor Kit – Predict Food Spoilage Before It Happens
Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.
Detect volatile organic compounds from food samples using a three-sensor array on Raspberry Pi 5, then train and deploy a TensorFlow Lite model that predicts days until spoilage. Designed for students and researchers tackling food waste challenges, this kit transforms raw sensor data into actionable freshness forecasts — all on local storage with no cloud dependency for inference.
What You'll Build
You’ll assemble a portable scanning station that reads ethanol, methane, and general VOC concentrations from fruit, vegetables, or grain. The pre-trained TFLite model processes the sensor readings in real time and displays predicted freshness duration on a dashboard. The NVMe SSD ensures fast boot and quick model loading, while the ADS1115 provides precise 16-bit analog sampling from all three MQ sensors. This build is ready to expand into automated alerts, data logging, or integration into cold chain monitoring.
What You'll Learn
- Interfacing analog gas sensors with Raspberry Pi 5 via I2C ADC and calibrating their outputs
- Collecting multivariate time-series data and preprocessing it for machine learning on the edge
- Converting a TensorFlow model to TFLite and deploying it with optimised inference on a Pi 5
- Building a system-level IoT solution — from hardware wiring and power management to data visualisation on a local dashboard
Kit Contents
| Component | Quantity |
|---|---|
| Raspberry Pi 5 4GB | 1 |
| MQ-3 Ethanol | 1 |
| MQ-135 VOC | 1 |
| MQ-2 General | 1 |
| ADS1115 ADC | 1 |
| NVMe SSD 128GB | 1 |
| Pi 5 M.2 HAT+ | 1 |
| USB-C PSU | 1 |
| M-M Wires | 20 |
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
Built for B.Tech ECE/EEE students taking up AI-IoT capstone projects, Smart India Hackathon teams working on food tech challenges, and CBSE Class 11-12 learners exploring machine learning through hands-on sensor integration. ATL Tinkering Labs and IIT/NIT/VIT/BITS engineering clubs will find it a ready, curriculum-friendly way to teach edge computing and automated quality monitoring without chasing individual components.
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 inside the box to launch the AI companion that knows every wire and config file for this kit. WhatsApp support is also available as backup.
Do the sensors require soldering?
No. All connections use male-male jumper wires and standard GPIO/board headers. The MQ sensors come with breakout boards, and the ADC connects via I2C pins.
Can I test different food types beyond what the model was trained on?
Yes. The kit includes a data collection pipeline script; you can gather your own VOC readings and retrain the TFLite model for custom food profiles.
Does this kit need an internet connection to predict spoilage?
No. Inference runs entirely on the Raspberry Pi 5 using the NVMe SSD for quick model access. Internet is only needed to fetch software updates or upload optional dashboard data.
Gas sensor array on Pi 5 detects volatile organic compound profiles from food samples — TFLite model predicts days until spoilage.
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