Medicinal Plant Detection App

An application for detecting medicinal plant species using a Convolutional Neural Network (CNN) based on the ResNet architecture. The app classifies over 200 medicinal plant species and provides an intuitive web interface for researchers and users.

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Tech Stack

• Backend: Flask
• Frontend: HTML, CSS, JavaScript
• Database: MySQL
• Machine Learning Framework: TensorFlow

Features

• Classification of 200+ medicinal plant species using a trained CNN model.
• Web interface for uploading plant images and viewing classification results.
• Streamlined data management with a MySQL database.
• User-friendly interface for researchers and professionals in the pharmaceutical industry.

Prerequisites

1. Python 3.x installed on your machine.
2. Install dependencies: pip install -r requirements.txt

markdown Copy code 3. Set up a MySQL database:

• Create a database named sih.
• Import the database schema:

  mysql -u root -p sih < database.sql
  

• Update db_config in app.py if required.

Usage

1. Run the Flask application: python app.py

markdown Copy code 2. Open your browser and navigate to http://127.0.0.1:5000/. 3. Upload an image of a medicinal plant to classify it.

Training the Model

The model was trained using the training.ipynb file:

1. Open training.ipynb in Jupyter Notebook.
2. Ensure that TensorFlow and the required dependencies are installed.
3. Follow the notebook steps to train the model and save weights to model.h5.

Deployment

This app can be deployed on any cloud platform supporting Flask applications, such as AWS EC2 or Heroku.

License

This project is licensed under the MIT License.

Contact

For questions or contributions, please reach out via GitHub.