| --- |
| title: LeafScan |
| emoji: 🌿 |
| colorFrom: green |
| colorTo: gray |
| sdk: docker |
| app_file: app.py |
| pinned: false |
| --- |
| |
|
|
| # 🌿 LeafScan — Plant Disease Detection using Deep Learning |
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| 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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| --- |
|
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| ## 🚀 Live Demo |
|
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| * 🌐 Hugging Face Space: https://huggingface.co/spaces/tktejask/leafscan |
|
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| --- |
|
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| ## 🧠 Project Overview |
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| LeafScan is a real-time plant disease detection system that: |
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| * Accepts **real-world leaf images** |
| * Detects **38 disease classes + 1 non-leaf class** |
| * Provides: |
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| * Disease name |
| * Confidence score |
| * Severity |
| * Description |
| * Treatment suggestion |
| * Top-5 predictions |
|
|
| --- |
|
|
| ## 📊 Dataset |
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| * 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 |
|
|
| --- |
|
|
| ## 🏗️ Model Architecture |
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|
| ``` |
| 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 |
| ``` |
|
|
| --- |
|
|
| ## ⚙️ Training Strategy |
|
|
| | Phase | Description | |
| | ------- | -------------------------- | |
| | Phase 1 | Train only classifier head | |
| | Phase 2 | Unfreeze last layers | |
| | Phase 3 | Full fine-tuning | |
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| Techniques used: |
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| * Transfer Learning |
| * Test Time Augmentation (TTA ×6) |
| * AdamW optimizer |
| * Label smoothing |
| * Class balancing |
|
|
| --- |
|
|
| ## 🔬 Inference Pipeline |
|
|
| ``` |
| Input Image |
| ↓ |
| Preprocessing (Resize → Normalize) |
| ↓ |
| Model Prediction |
| ↓ |
| TTA Averaging |
| ↓ |
| Confidence + Decision Logic |
| ↓ |
| Final Output + Top-5 Classes |
| ``` |
|
|
| --- |
|
|
| ## 🧪 Features |
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| * ✅ 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 |
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| ### 1. Setup |
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| ```bash |
| python -m venv venv |
| venv\Scripts\activate |
| pip install -r requirements.txt |
| ``` |
|
|
| --- |
|
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| ### 2. Run server |
|
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| ```bash |
| 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 |
| ``` |
|
|
| --- |
|
|
| ## 🌐 Hugging Face Deployment |
|
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| * Platform: Hugging Face Spaces |
| * Runtime: Flask (Docker/Spaces) |
| * URL: https://huggingface.co/spaces/tktejask/leafscan |
|
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| ### What was done: |
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| * Uploaded model + backend + frontend |
| * Configured app.py to run on port 7860 |
| * Added README config block |
|
|
| --- |
|
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| ## 🔗 API Endpoints |
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| | 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 |
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| * Accuracy: ~96% (on PlantVillage test set) |
| * Supports: 38 disease classes |
| * Handles real-world noise via TTA |
|
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| --- |
|
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| ## ⚠️ Limitations |
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| * 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) |
|
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| * 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** |
|
|
| --- |
|
|
| ## 📦 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 |
|
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| * Python |
| * PyTorch |
| * timm |
| * Flask |
| * HTML/CSS/JS |
| * Hugging Face Spaces |
|
|
| --- |
|
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| ## 📄 License |
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| Educational project. Dataset is public (PlantVillage). |
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|
