| # CropGuard GH 🌿 |
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| Image-Based Crop Disease Detection System for Smallholder Farmers in Ghana. |
| Final Year Project — Oppong David, BTech Computer Technology, Kumasi Technical University. |
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| ## Two ways to run it |
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| 1. **Instant / no install** — open `cropguard.html` on any phone or browser. |
| Covers all 14 Ghanaian crops via the model; works offline as an estimate (English + Twi). Can also connect |
| to the trained model — see Settings / docs. |
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| 2. **Full production system** — the real Chapter 3 stack: |
| - `backend/train.py` — train the MobileNetV2 model |
| - `backend/app.py` — FastAPI inference server |
| - `backend/recommendations.json` — treatment knowledge base |
| - `frontend/src/CropGuard.jsx` — React frontend |
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|
| ## Quick start (backend) |
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|
| ```bash |
| cd backend |
| pip install -r requirements.txt |
| python train.py --data ./data # train (needs a dataset) |
| uvicorn app:app --port 8000 # serve |
| ``` |
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| Full details, API reference and report mapping: **`docs/DOCUMENTATION.md`**. |
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