_{Created by Shubham Kumar and other contributors}

Models are trained on the preprocessed dataset which can be downloaded here.

• It is recommended to set up the project inside a virtual environment to keep the dependencies separated.
  • Python
  • Conda
• Activate your virtual environment.
• Install dependencies by running pip install -r requirements.txt.
• Start up the server by running python app/server.py serve.
• Visit http://localhost:8080/ to explore and test.

Make Sure the Docker is installed in your local Machine. Click Here to know that how to install Docker.

• Mac:

  $ git clone https://github.com/imskr/Plant_Disease_Detection.git
  $ cd Plant_Disease_Detection
  $ docker build -t fastai-v3 .
  $ docker run --rm -it -p 8080:8080 fastai-v3

  Go to http://localhost:8080/ to test your app.

• Windows:

  $ git clone https://github.com/imskr/Plant_Disease_Detection.git
  $ cd Plant_Disease_Detection
  $ docker build -t fastai-v3 .
  $ docker run --rm -it -p 8080:8080 fastai-v3

  Go to http://localhost:8080/ to test your app.

  Note: Windows 10 Pro required.

• Linux:

  $ git clone https://github.com/imskr/Plant_Disease_Detection.git
  $ cd Plant_Disease_Detection
  $ docker build -t fastai-v3 .
  $ docker run --rm -it -p 8080:8080 fastai-v3
  

  Note: If this doesn't work use --no-cache flag in the build command.

  Go to http://localhost:8080/ to test your app.

• Google Cloud Platform:

  The complete guideline to deploy the Plant Disease Detection App can be found here

• AWS Elastic BeanStalk:

  The complete guideline to deploy the Plant Disease Detection App can be found here

• Google Cloud Platform (Intermediate) - The complete tutorial can be found here

• Gradient (Easy) - The complete tutorial can be found here

• AWS EC2 (Advance) - The complete tutorial can be found here

Before making your valuable contribution to this project do check CONTRIBUTING.md file.

When using any part of this repo, please cite: Plant Village Paper.