README.md

metadata

title: LeafScan
emoji: 🌿
colorFrom: green
colorTo: gray
sdk: docker
app_file: app.py
pinned: false

🌿 LeafScan — Plant Disease Detection using Deep Learning

A full-stack AI application that detects plant leaf diseases from real-world images using a fine-tuned EfficientNetB3 model trained on the PlantVillage dataset.

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🚀 Live Demo

• 🌐 Hugging Face Space: https://huggingface.co/spaces/tktejask/leafscan

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🧠 Project Overview

LeafScan is a real-time plant disease detection system that:

• Accepts real-world leaf images

• Detects 38 disease classes + 1 non-leaf class

• Provides:

  • Disease name
  • Confidence score
  • Severity
  • Description
  • Treatment suggestion
  • Top-5 predictions

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📊 Dataset

• Source: PlantVillage Dataset
• Link: https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset
• Size: ~54,000 images
• Classes: 38 diseases + healthy + 1 synthetic "not a leaf" class

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🏗️ Model Architecture

Input Image (300×300)
        ↓
EfficientNetB3 (Pretrained on ImageNet)
        ↓
Feature Vector (1536)
        ↓
Custom Head:
  Dense → GELU → Dropout
  Dense → GELU → Dropout
  Output Layer (39 classes)
        ↓
Softmax Probabilities

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⚙️ Training Strategy

Phase	Description
Phase 1	Train only classifier head
Phase 2	Unfreeze last layers
Phase 3	Full fine-tuning

Techniques used:

• Transfer Learning
• Test Time Augmentation (TTA ×6)
• AdamW optimizer
• Label smoothing
• Class balancing

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🔬 Inference Pipeline

Input Image
   ↓
Preprocessing (Resize → Normalize)
   ↓
Model Prediction
   ↓
TTA Averaging
   ↓
Confidence + Decision Logic
   ↓
Final Output + Top-5 Classes

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🧪 Features

• ✅ Works on real-world images (not just dataset)
• ✅ Detects non-leaf images
• ✅ REST API support
• ✅ Beautiful frontend UI
• ✅ Deployable locally + cloud

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🖥️ Local Deployment

1. Setup

python -m venv venv
venv\Scripts\activate
pip install -r requirements.txt

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2. Run server

python app.py

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3. Output

Model ready.
 * Running on http://127.0.0.1:7860

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4. Open in browser

http://localhost:7860

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🌐 Hugging Face Deployment

• Platform: Hugging Face Spaces
• Runtime: Flask (Docker/Spaces)
• URL: https://huggingface.co/spaces/tktejask/leafscan

What was done:

• Uploaded model + backend + frontend
• Configured app.py to run on port 7860
• Added README config block

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🔗 API Endpoints

Endpoint	Description
/api/predict	Upload image
/api/predict-url	Predict from URL
/api/predict-base64	Predict from base64
/api/classes	List classes
/api/health	Server status

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📈 Model Performance

• Accuracy: ~96% (on PlantVillage test set)
• Supports: 38 disease classes
• Handles real-world noise via TTA

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⚠️ Limitations

• Trained on controlled dataset → real-world variation may reduce accuracy
• Needs clear leaf image
• Heavy model → slow on CPU

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🔥 Key Highlights (Interview Points)

• Built end-to-end ML system
• Used transfer learning (EfficientNetB3)
• Implemented TTA for robustness
• Designed Flask API + frontend integration
• Deployed on Hugging Face Spaces
• Handled real-world inference issues

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📦 Project Structure

leaf scan/
├── app.py
├── model.py
├── predict.py
├── metrics.py
├── models/
│   └── best_model.pth
├── data/
│   └── classes.txt
├── frontend/
│   └── index.html
└── requirements.txt

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🛠️ Tech Stack

• Python
• PyTorch
• timm
• Flask
• HTML/CSS/JS
• Hugging Face Spaces

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📄 License

Educational project. Dataset is public (PlantVillage).