# MangoGuard — Edge AI That Detects Mango Diseases in the Field

> Source: <https://dev.to/eyobel_z/mangoguard-edge-ai-that-detects-mango-diseases-in-the-field-h93>
> Published: 2026-06-03 03:53:06+00:00

*Submission for the GitHub Finish-Up-A-Thon Challenge*

Mango farming is a lifeline for millions of smallholder farmers in Ethiopia.

A single fungal outbreak can silently destroy 20–30% of a harvest before a

farmer even recognises it. Existing solutions require a lab, a specialist,

or reliable internet. None of those exist where the problem is worst.

**MangoGuard runs AI directly on a microcontroller smaller than a credit card
— no cloud, no Wi-Fi, no lab.**

An **Arduino Nano 33 BLE Sense** runs a quantized MobileNetV1 model that

classifies mango leaf disease in under 2 seconds at 86.45% accuracy on real

Ethiopian farm data. The Nano also reads live temperature and humidity via a

DHT22 sensor and sends everything — disease result, temp, and humidity — to a

**Raspberry Pi 4 gateway**, which evaluates environmental disease risk against

agronomic thresholds, runs a 24-hour AI forecast model, and **generates
plain-language recommendations** pushed directly to farmers based on current

Everything streams to a React dashboard in real time via WebSocket.

From the dashboard you can:

The dashboard is fully bilingual — **English and Amharic (አማርኛ)** — because

agronomists advising Ethiopian farmers shouldn't have to work in a language

that isn't theirs.

Under the hood, every scan is saved to a PostgreSQL database. The **admin
dashboard** exposes this data as a labeled dataset that can be used to

**🌐 Live Demo:** [https://mango-guard.vercel.app/](https://mango-guard.vercel.app/)

**📂 GitHub:** [https://github.com/SCIFI-Shinobi/Intelligent-Mango-Health-Monitoring](https://github.com/SCIFI-Shinobi/Intelligent-Mango-Health-Monitoring)

The hackathon prototype worked — but only on my machine, with undocumented

secrets, and no way for anyone else to run it. Here's what I shipped to fix that:

`ARCHITECTURE.md`

, `DEPLOYMENT.md`

, and `CONTRIBUTING.md`

`.gitignore`

which was blocking `.env.example`

files from being committed`scanIntervalMs = 3600000UL`

, forecast threshold ≥ 24.A stranger can now fork, configure, and deploy this in under 20 minutes.

Copilot saved the most time on the backend — `main.py`

grew past 3,000 lines

and it was excellent at continuing repetitive patterns: email templates,

database migration helpers, route structure. Once I wrote the first, it nailed

the second.

During the polish phase, asking Copilot to review the README flagged that I

had no troubleshooting section and no env variable docs — exactly what a

first-time contributor needs. It also helped clean up firmware comments after

I fixed the production bug.

The honest limitation: it continues patterns well but won't proactively tell

you what's missing. You have to ask the right question first.

*Built to bridge the gap between AI and smallholder agriculture in Ethiopia. 🌱*