The main objective of this project is to create a mobile application that leverages the capabilities of a Convolutional Neural Network (CNN) to predict plant details from images. This user-friendly tool caters to plant enthusiasts and nature lovers, offering two primary functionalities: real-time image recognition via the camera feature and image analysis from the device's gallery.

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• Users can capture images of plants in real-time using their device's camera.
• The integrated TensorFlow model, trained on a dataset of plant images, analyzes the captured image.
• The deep learning model identifies the plant species and displays its name, description, and other relevant information to the user.

• Users can choose images from their device's gallery for plant recognition.
• The selected images undergo the same prediction process as real-time captures, providing plant details for personal image collections.

• The application features a database to store and retrieve plant details, including predictions made by the model.
• Users can easily access this information through the user interface.

To get started with this project, follow these steps:

1. Click the "Fork" button at the top right of this repository to create a copy in your own GitHub account.

2. Open your terminal or command prompt.

3. Navigate to the directory where you want to store the project on your local machine.

4. Use the following command to clone the repository to your local machine:

   git clone https://github.com/jayasuryard31/AyurScan.git

5. Replace yourusername with your GitHub username.

Navigate to the project's directory:

cd AyurScan
git add .
git commit -m "Add feature XYZ"  # Replace with an appropriate commit message
git push origin main  # If you are working on a different branch, replace 'main' with the name of your branch

This section provides a complete set of instructions for contributors on how to make changes, commit them, push to their forked repository, and create a pull request to merge their changes into the original project repository.

This project is licensed under the MIT License.

This project is a complex endeavor that requires the collaborative efforts of React Native developers and machine learning experts to bring this ambitious plant recognition application to life.

👥 Collaborators: