Hwang713/leaf-disease-EfficientNetV2B1

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

  

🌿 Plant Disease Detection with EfficientNetB1

Plant-Disease-Detection-EfficientNetB1 is a deep learning-powered app that identifies 15 types of plant diseases from leaf images using a fine-tuned EfficientNetB1 model trained on the PlantVillage dataset.

📸 Upload an image, paste from clipboard, or use webcam input Get instant predictions on plant disease type — with confidence score and class label.

🌐 Demo

Deployed on Hugging Face Spaces: 

🧠 Model Details

• —Model: EfficientNetB1 (Keras, Sequential API)
• —Classes: 15 plant disease types
• —Input Size: 240×240
• —Preprocessing: Normalization (0–1), real-time data augmentation
• —Training:
• —Phase 1: Freeze base, train classification head
• —Phase 2: Unfreeze all, fine-tune at low learning rate (1e-5)
• —Validation Accuracy: ~97%

📓 Training Notebook (Kaggle)

The model was trained using TensorFlow and Keras on Kaggle. 🔗 fine-tuning-efficientnetb1-plantdiseasedetection Includes:

• —Stratified 80/20 data split using ImageDataGenerator
• —Real-time augmentations
• —Early stopping and checkpointing (best weights only)
• —Final .h5 weights file exported

Features

• —Classifies 15 plant diseases
• —Supports upload, webcam, and clipboard paste
• —Outputs predicted class name and confidence score
• —Lightweight & fast inference — powered by Gradio

⚠️ Grad-CAM Note

Grad-CAM is disabled in this version due to using Sequential() with load_weights() To enable Grad-CAM, re-train with Functional API and access the last convolutional layer output.

📁 Folder Structure

12_PlantDiseaseDetection-HF/
├── app.py                   # Gradio interface
├── inference_utils.py       # Model loading + prediction
├── model/
│   ├── efficientnetb1_plant_final.weights.h5
│   └── class_names.json
├── assets/                  # Demo/test images
├── requirements.txt
└── README.md

Example Output

Input Image	Prediction
	🍅 Tomato - Early Blight (99%)

Installation

To run locally:

git clone https://github.com/McKlay/plant-disease-type-EfficientNetB1
cd plant-disease-type-EfficientNetB1
pip install -r requirements.txt
python app.py

Requirements

tensorflow
gradio
opencv-python
numpy
Pillow

👨‍💻 Author

Developed by Clay Mark Sarte Built with TensorFlow + Gradio Deployed to Hugging Face Spaces

⚠️ Disclaimer

This tool is for educational use only. Not intended for use in real-world agricultural decisions or medical treatment.