Imskr Plant Disease Detection Save

Plant Disease Detector Web Application

Project README

Plant Disease Detector



Created by Shubham Kumar and other contributors



My Article in TowardsDataScience

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

Local Set-Up

Local:

  • It is recommended to set up the project inside a virtual environment to keep the dependencies separated.
  • 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.

Docker:

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.

Deployment

  • 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

Server Set-Up (For Training)

  • 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

Dataset Description:

Name No of Classes Class Names
Apple 04 'Apple___Apple_scab','Apple___Black_rot','Apple___Cedar_apple_rust' 'Apple___healthy'
Blueberry 01 'Blueberry___healthy'
Cherry 02 'Cherry_(including_sour)Powdery_mildew', 'Cherry(including_sour)_healthy'
Corn 04 'Corn___Cercospora_leaf_spot', 'Corn___Common_rust','Corn___Northern_Leaf_Blight','Corn___healthy'
Grape 04 'Grape___Black_rot','Grape___Esca_(Black_Measles)','Leaf_blight_(Isariopsis_Leaf_Spot)','Grape___healthy'
Orange 01 'Orange___Haunglongbing_(Citrus_greening)'
Peach 02 'Peach___Bacterial_spot','Peach___healthy'
Pepper 02 'Pepper,_bell___Bacterial_spot','Pepper,_bell___healthy'
Potato 03 'Potato___Early_blight','Potato___Late_blight','Potato___healthy'
Raspberry 01 'Raspberry___healthy'
Soyabean 01 'Soybean___healthy'
Squash 01 'Squash___Powdery_mildew'
Strawberry 02 'Strawberry___Leaf_scorch','Strawberry___healthy'
Tomato 10 Tomato: 'Bacterial_spot','Early_blight', 'Late_blight', 'Leaf_Mold', 'Septoria_leaf_spot', 'Spider_mites','Target_Spot', 'Yellow_Leaf_Curl_Virus', 'Mosaic_virus', 'Healthy'

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

Citation

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


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