https://github.com/mittalbhavya/dhanvantrimycut

dhanvantri is an Android app developed in Kotlin, using TensorFlow for machine learning. It identifies medicinal plants from images taken with the device's camera or selected from the gallery and provides detailed information about the plants. The app works offline and features a user-friendly interface.
https://github.com/mittalbhavya/dhanvantrimycut

ai androidapp-tensorflow healthtech imageprocessing innovativetech kotlin machinelearning

Last synced: about 1 month ago
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dhanvantri is an Android app developed in Kotlin, using TensorFlow for machine learning. It identifies medicinal plants from images taken with the device's camera or selected from the gallery and provides detailed information about the plants. The app works offline and features a user-friendly interface.

• Host: GitHub
• URL: https://github.com/mittalbhavya/dhanvantrimycut
• Owner: MITTALBHAVYA
• Created: 2024-03-11T07:09:03.000Z (10 months ago)
• Default Branch: master
• Last Pushed: 2024-07-11T17:57:54.000Z (5 months ago)
• Last Synced: 2024-07-12T14:09:27.472Z (5 months ago)
• Topics: ai, androidapp-tensorflow, healthtech, imageprocessing, innovativetech, kotlin, machinelearning
• Language: Kotlin
• Homepage:
• Size: 42.4 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Dhanvantri : 

Medicinal Plant Recognition Android Application

This Android application is developed in Kotlin and utilizes machine learning models implemented with the TensorFlow library to recognize medicinal plants. The app aims to assist users in identifying various medicinal plants commonly found in our surroundings.

Features

Medicinal Plant Recognition: Users can capture images of medicinal plants using their device's camera or choose images from the gallery. The application then processes these images using a machine learning model to identify the plant species and provide relevant information.

Information Display: Upon successful identification, the application displays detailed information about the recognized medicinal plant. This information may include the plant's common name, scientific name, medicinal uses, cultivation tips, and more.

User-Friendly Interface: The application features an intuitive and user-friendly interface designed to provide a seamless experience for users of all levels.

Offline Support: The machine learning model is integrated into the application, allowing it to work offline without requiring an internet connection for plant recognition.

Technologies Used

Kotlin: The primary programming language used for developing the Android application.

TensorFlow: An open-source machine learning framework used for implementing the machine learning model responsible for plant recognition.

Android Studio: The official integrated development environment (IDE) for Android application development.

Android Camera API: Utilized for capturing images directly from the device's camera.

Android Gallery API: Used for selecting images from the device's gallery.

JSON Data: Plant information is stored and retrieved using JSON format.

Usage

Capture or Select Image: Use the camera to capture an image of a medicinal plant or select an image from the device's gallery.

Image Processing: The application processes the captured or selected image using the machine learning model to identify the medicinal plant.

View Information: Once the plant is successfully identified, the application displays detailed information about the plant on the screen.

Explore Further: Users can explore additional information about the medicinal plant, such as its uses, cultivation techniques, and more.

Credits

Developed by Bhavya Mittal, Anand Pratap Singh, Mehak, Deepanshu

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Disclaimer

While the application aims to provide accurate information about medicinal plants, it is intended for educational and informational purposes only. Users should consult with qualified professionals before using any plants for medicinal purposes. The developers of this application are not responsible for any misuse or adverse effects resulting from the use of the information provided by the application.