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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:
- Click the "Fork" button at the top right of this repository to create a copy in your own GitHub account.
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Open your terminal or command prompt.
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Navigate to the directory where you want to store the project on your local machine.
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Use the following command to clone the repository to your local machine:
git clone https://github.com/jayasuryard31/AyurScan.git
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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 branchThis 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:
