🌿 LeafScan — Plant Disease Detection

AI-powered plant disease detection from leaf images. Upload a photo and get instant diagnosis with treatment recommendations.

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Features

• 🖼️ Drag-and-drop image upload with instant preview
• 🔬 38 disease classes across 14 crop species (PlantVillage dataset)
• 🧠 EfficientNetB3 deep learning model with transfer learning
• 💊 Treatment recommendations for every detected disease
• 📊 Confidence scores with animated progress bars
• 📱 Responsive design — works on desktop and mobile
• 🐳 Docker support for one-command deployment

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Quick Start (Development)

Prerequisites

• Python 3.11+ and pip
• Node.js 20+ and npm

1. Backend

cd backend
pip install -r requirements.txt
uvicorn app.main:app --reload --port 8000

⚠️ The backend will print a warning if no trained model is found at backend/model/plant_disease_model.h5. The /predict endpoint returns HTTP 503 until a model is provided.

2. Frontend

cd frontend
npm install
npm run dev

Open http://localhost:3000 in your browser.

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Training the Model

cd backend
python train.py /path/to/plantvillage/dataset --epochs 10

The dataset directory should have one sub-folder per class:

dataset/
├── Apple___Apple_scab/
├── Apple___Black_rot/
├── ...
└── Tomato___healthy/

The trained model is saved to backend/model/plant_disease_model.h5.

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Docker Deployment

docker-compose up --build

Service	URL
Frontend	http://localhost:3000
Backend	http://localhost:8000
Health	http://localhost:8000/health

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Project Structure

leafscan/
├── backend/
│   ├── app/
│   │   ├── main.py               # FastAPI entry point
│   │   ├── routes/predict.py     # /predict and /health endpoints
│   │   ├── services/model.py     # Model loading & inference
│   │   ├── services/disease_info.py  # 38-class disease mapping
│   │   └── utils/image_utils.py  # Image preprocessing
│   ├── model/                    # Trained model (gitignored)
│   ├── train.py                  # Model training script
│   ├── requirements.txt
│   └── Dockerfile
├── frontend/
│   ├── src/
│   │   ├── components/           # Navbar, ImageUploader, ResultCard
│   │   ├── pages/                # Home, About
│   │   ├── api/predict.js        # Axios API client
│   │   ├── App.jsx
│   │   └── main.jsx
│   ├── tailwind.config.js
│   ├── package.json
│   └── Dockerfile
├── docker-compose.yml
└── README.md

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Tech Stack

Layer	Technology
Frontend	React 18, Vite, Tailwind CSS
Backend	Python 3.11, FastAPI, Uvicorn
ML Model	TensorFlow / Keras, EfficientNetB3
Deploy	Docker, Docker Compose, Nginx

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API Reference

GET /health

Returns {"status": "ok"}.

POST /predict

Parameter	Type	Description
file	File	Leaf image (JPEG/PNG)

Response:

{
  "predicted_class": "Tomato___Early_blight",
  "confidence": 0.97,
  "plant": "Tomato",
  "disease": "Early Blight",
  "is_healthy": false,
  "treatment": "Apply copper-based fungicide. Remove affected leaves."
}

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License

MIT