MangoGuard — Edge AI That Detects Mango Diseases in the Field

A developer built MangoGuard, an edge AI system that detects mango diseases directly on an Arduino Nano microcontroller without requiring cloud connectivity or internet access. The quantized MobileNetV1 model classifies mango leaf disease in under two seconds with 86.45% accuracy on real Ethiopian farm data, while a Raspberry Pi gateway evaluates environmental disease risk and generates plain-language recommendations. The system streams results to a bilingual English and Amharic React dashboard via WebSocket, with all scan data stored in PostgreSQL for use as a labeled dataset.

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. 🌱