Flora Vision

Technologies Used

BackEnd:

• Python
• Tensorflow
• Keras
• Flask

FrontEnd:

• React
• Axios
• Framer-motion

Project Description

This project focuses on utilizing machine learning techniques to identify various medicinal plants and provide users with relevant information. Below are the key components of the project:

• CNN Model Training: We have trained a Convolutional Neural Network (CNN) model using TensorFlow and Keras. The model is trained on a dataset containing images of medicinal plants.

• Flask API: To deploy the trained model and make predictions accessible, we have built a Flask API. This API serves as the interface for users to interact with the model.

• Plant Classes: The model can successfully identify images belonging to six different classes of medicinal plants. These classes are:

  • Arjuna
  • Bramhi
  • Curry
  • Mint
  • Neem
  • Rubble

• Prediction and Information Retrieval: Upon successful prediction, users receive information about the identified medicinal plant. This information includes various attributes such as medicinal properties, usage, and precautions.

• Chatbot Integration: Additionally, we have integrated a chatbot feature to allow users to ask questions related to the identified plant. The chatbot provides informative responses based on the user's queries.

Installation

1. Clone the repository:

   git clone https://github.com/swarup-2004/Flora-Vision.git
   cd Flora-Vision

2. Install dependencies:

   pip install -r requirements.txt
   npm install

Usage

1. Start server:

   cd Backend
   python app.py

2. Run Application:

    npm run dev

3. Upload image of Plant and get result

4. Ask Questions to chatbot

Contributors

• Atharva Zanjad
• Swarup Pokhakar
• Tanmay Shingavi
• Vaishnavi Thakur

Screenshots