/Plant_Disease_Detection

Plant Disease Detector Web Application

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0

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.


Buy Me A Coffee