Fridge Freshness Vision · A fridge that knows how fresh your food is
A time-series visual freshness system for apples, eggs, leafy greens and dairy: ESP32-S3 edge nodes + cloud vision LLMs (dual-vendor primary/backup) + a time-series scoring engine outputting a 0–100 freshness score and four states. Solution partnership for fridge OEMs and integrators, open for crowdfunding participation.
- 🎯Crowdfunding goal from ¥20,000
- 💰Solution partnership for OEMs & integrators
What it brings you
0–100 freshness score
Baseline vs daily photos compared over time; four states (fresh / slight wilt / partial spoilage / spoiled) + estimated remaining shelf days.
Dual-vendor vision LLMs
Qwen-VL primary + Doubao vision backup with automatic failover; Wenxin VL / GLM-4V on the candidate list.
Harsh imaging handled
Optimized for low light, condensation fog and packaging glare: fill-light 3-frame burst + anti-fog lens.
ESP32-S3 edge nodes
Door-magnet / scheduled dual triggers, offline caching with resync, deep-sleep power saving.
Time-series scoring
EMA smoothing, rebound clamping and jump re-checks; strong output validation (≥99% format compliance).
Cost under control
Monthly budget circuit-breaker and full call accounting; hard cases auto-saved as training material for the phase-2 in-house model.
Transparent R&D progress
The percentage is assessed from completed milestones across the whole development plan, and updated as development moves forward.
✅ Completed
- ✅Cloud recognition service complete (FastAPI + admin console, runnable, mock demo supported)
- ✅86 automated test cases (cloud 60 + legacy gateway 25 + edge policy 1)
- ✅Dual-vendor vision-LLM automatic failover pipeline working
- ✅ESP32-S3 edge-node firmware complete (ESP-IDF 5.x)
- ✅Phase-2 groundwork: RK3588S local inference + YOLOv8 in-house training pipeline reserved
🚧 In progress / next
- ⬜Hardware prototype integration and real-scenario evaluation set collection
- ⬜Prompt and scoring-strategy tuning on real data
- ⬜Phase-2 on-device in-house model (RK3588S + YOLOv8) kickoff
Key technology
| Coverage | Core 4 categories: apples / eggs / leafy greens / dairy; plus 20+ common food types |
|---|---|
| Edge node | ESP32-S3-N16R8 + OV5640 5MP anti-fog lens + fill light |
| Cloud | Python 3.10 + FastAPI + SQLite; dual-vendor vision-LLM API primary/backup |
| Scoring | 0–100 score + 4 states + estimated shelf days; EMA smoothing / rebound clamp / jump re-check |
| Model | Subsystem solution partnership for fridge OEMs / integrators; private deployment supported |
| Roadmap | Phase 2 switches to on-device in-house model (RK3588S 6-TOPS + YOLOv8) with zero business-system changes |
Back the Fridge Freshness Vision crowdfunding
Early access · Founder pricing
Crowdfunding backers get priority access to engineering samples and founder pricing, plus first updates on mass-production schedules.
Co-development · Custom
Enterprises can co-develop on any active project: custom sensors, enclosures, apps and cloud platforms — sharing the R&D outcome.
Channel · Industrial partners
Distributors, system integrators and industrial investors are invited to participate via crowdfunding or strategic partnership.
Who is this project for?
How is recognition accuracy ensured?
We put risks on the table
Crowdfunding means sharing development uncertainty with us — please read these before backing.
- Field-tuning risk: long-cycle accuracy in real fridges (fog, glare, occlusion) requires prototype integration; evaluation data will be published on the project page.
- LLM service dependence: cloud recognition uses third-party vision APIs; dual-vendor failover mitigates outages and the edge version removes dependence entirely.
- Category coverage: 4 core categories are finely calibrated; accuracy is not promised for items outside the supported list.
- Delivery timeline: the 9-week standard schedule may extend depending on prototype and data availability; milestone-based and transparent.
Got an idea? A machine to make smart?
From a brand-new AI device to retrofitting existing equipment — talk directly with our engineers. Bilingual service in English & Chinese.
📞 Phone / WeChat: 19040667970