CropGuard: Leaf Disease Detector

Use online at HuggingFace Spaces at: https://huggingface.co/spaces/mitraarka27/CropGuard

CropGuard is a lightweight, deployable machine learning app that detects leaf diseases in Potato, Tomato, and Grape plants from user-uploaded or captured images.

Built using PyTorch, Gradio, Docker, and Hugging Face Spaces, it provides the following capabilities:

• Upload or capture a leaf image
• Predict plant health status
• Identify likely disease (if any)
• Visualize model attention using GradCAM++ heatmaps
• Provide quick disease information and treatment suggestions

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Project Structure

CropGuard/
├── app.py                # Gradio app
├── Dockerfile            # Docker container definition
├── requirements.txt      # Python dependencies
├── notebooks/            # Step-by-step project development
│   ├── 01_data_preprocessing.ipynb
│   ├── 02_model_training.ipynb
│   ├── 03_model_validation.ipynb
│   └── 04_gradcam_visualization.ipynb
├── src/                  # Source code (organized into modules)
│   ├── app/
│   ├── data/
│   ├── model/
│   └── utils/
├── sample_images/        # Few test images (optional for demo)
├── disease_info.json     # Disease descriptions
└── README.md             # (this file)

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Notebooks Overview

Notebook	Purpose
01_data_preprocessing.ipynb	Download PlantVillage dataset, clean and split into train/val/test sets
02_model_training.ipynb	Set up data augmentation, train MobileNetV2 model, monitor training curves
03_model_validation.ipynb	Evaluate model performance, generate metrics, confusion matrix
04_gradcam_visualization.ipynb	Generate GradCAM++ heatmaps to visualize model focus

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How to Run Locally

1. Clone the repo:

git clone https://github.com/mitraarka27/CropGuard.git
cd CropGuard

2. Create a virtual environment:

python3 -m venv .venv
source .venv/bin/activate

3. Install dependencies:

pip install --upgrade pip
pip install -r requirements.txt

4. Launch the app:

python app.py

It will be available at http://localhost:7860.

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How to Build and Run with Docker

docker build -t cropguard-app .
docker run -p 7860:7860 cropguard-app

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Web Deployment

Easily deployable on:

• Hugging Face Spaces
• DockerHub

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Sample Images

We provide a few sample leaf images in the sample_images/ directory so users can test the model even without their own images.

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License

MIT License.

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Acknowledgments

• Dataset: PlantVillage Dataset
• Base Model: MobileNetV2 (pretrained on ImageNet)
• Visualization: GradCAM++

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Author

Made by Arka Mitra © 2025.

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