Table of Contents
GreenGuard is an innovative image classification project designed to revolutionize agriculture by providing farmers with an automated solution for the early detection of plant diseases. With the aim of mitigating crop losses and optimizing agricultural practices, GreenGuard leverages a convolutional neural network to identify and classify diseased plants into three different categories: healthy, prone to disease, and diseased.
To build this project, I've started by outlining the programming languages that form its foundation, followed by an in-depth exploration of the libraries incorporated. This deliberate documentation not only promotes transparency but also serves as a comprehensive reference to the technologies leveraged throughout the development journey.
- Programming Language : Python
- Libraries: Tensorflow, keras, PIL, Pandas, Numpy, Matplotlib, Plotly, Streamlit
This section provides guidance on configuring this project on your local machine and running the Streamlit application locally.
To run this project, ensure that your PC/Laptop has the following prerequisites:
- Python
- Integrated Development Environment (IDE) such as PyCharm or Visual Studio Code.
To initialize this project on your local machine, kindly adhere to the outlined instructions provided herewith. By following these meticulously crafted steps, you will seamlessly configure the project environment for optimal functionality on your personal workstation. Your cooperation in adhering to these guidelines is greatly appreciated.
- Create a new virtual environment by using the command
conda create -p venv python=3.10 -y
- Activate the newly created virtual environment
conda activate venv/
- Install all the required project dependencies by executing the provided command. Subsequent to running this command, the internal setup file will be invoked, facilitating the configuration of your project by identifying and installing the necessary packages.
pip install -r requirements.txt
- After successfully installing all essential dependencies, proceed to run Streamlit locally by executing the following command:
streamlit run Strealmit/Home.py
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE.txt for more information.
- Twitter Handle: @Singh_yuvraaj1
- Linkedin Handle: Yuvraj Singh
- Mail: [email protected]