muditmahajan21/Plant-Disease-Detection-Web-application

Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box. Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention. In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. While several visualization methods were used as they are, others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs. Moreover, by interpreting the generated attention maps, we identified several layers that were not contributing to inference and removed such layers inside the network, decreasing the number of parameters by 75% without affecting the classification accuracy. The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis.

Plant Disease Detection Web Application:

• Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box.
• Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention.
• In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making.
• While several visualization methods were used as they are, others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs. Moreover, by interpreting the generated attention maps, we identified several layers that were not contributing to inference and removed such layers inside the network, decreasing the number of parameters by 75% without affecting the classification accuracy.
• The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis.

Head on to How Convolutional Neural Networks Diagnose Plant Disease for the complete article.

Local Set-Up

Local:

• 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.

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'