https://github.com/goldsharon/identification-of-medicinal-plants

Deep Learning CNN Model used to identify medicinal plants. Implementation of flask framework web application
https://github.com/goldsharon/identification-of-medicinal-plants

Last synced: 25 days ago
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Deep Learning CNN Model used to identify medicinal plants. Implementation of flask framework web application

• Host: GitHub
• URL: https://github.com/goldsharon/identification-of-medicinal-plants
• Owner: GoldSharon
• Created: 2023-10-10T13:22:32.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-02T13:26:58.000Z (about 1 month ago)
• Last Synced: 2024-12-02T14:33:14.194Z (about 1 month ago)
• Language: HTML
• Homepage:
• Size: 16.6 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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

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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

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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](LICENSE).

## Contact

For questions or contributions, please reach out via [GitHub](https://github.com/GoldSharon).