Plant-disease-detection

About the Project

An application that for farmers to detect the type of plant or crops, detect any kind of diseases in them. The app sends the image of the plant to the server where it is analysed using CNN classifier model. Once detected, the disease and its solutions are displayed to the user

Model

Trained to identify 5 classes for Disease Detection and 24 classes for Disease Classification. Dataset can be downloaded form kaggle

       - Disease Classification Classes

                   - Apple___Apple_scab
                   - Apple___Black_rot
		   - Apple___Cedar_apple_rust
		   - Apple___healthy
		   - Blueberry___healthy
		   - Cherry___healthy
		   - Cherry___Powdery_mildew
		   - Grape___Black_rot
		   - Grape___Esca_Black_Measles
		   - Grape___healthy
		   - Grape___Leaf_blight_Isariopsis_Leaf_Spot
		   - Orange___Haunglongbing
		   - Peach___Bacterial_spot
		   - Peach___healthy
		   - Pepper,_bell___Bacterial_spot
		   - Pepper,_bell___healthy
		   - Potato___Early_blight
		   - Potato___healthy
		   - Raspberry___healthy
		   - Soybean___healthy
		   - Squash___Powdery_mildew
		   - Strawberry___healthy
		   - Strawberry___Leaf_scorch
		
        - Disease Detection Classes
        
		   - Cherry___healthy
		   - Cherry___Powdery_mildew
		   - Grape___Black_rot
		   - Grape___Esca_Black_Measles
		   - Grape___healthy
		   - Grape___Leaf_blight_Isariopsis_Leaf_Spot 

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Cloning the project

• Run command git clone "https://github.com/Saideepthi123/Plant-disease-detection.git" and change into the project folder
• Create a virtual environment env in the repository (use virtualenv, etc)
• Activate virtual environment
• Install the requirements

To create virtual environment and install requirements run following commands

virtualenv env

To activate the environment use following commands: Window:

.\env\Scripts\activate

Ubuntu/Linux

source env/bin/activate

pip install -r requirements.txt

Command to run the app

• streamlit run app.py

Demo

• About

  

• Disease Predection

  

  • Image Upload

    

  • Image Detected

  

• Disease Classification

  • Image Upload

    

  • Image Classified

    

• Treatement Page

  

Required Libraries

• opencv-contrib-python-headless
• tensorflow-cpu
• streamlit
• numpy
• pandas
• pillow
• keras
• matplotlib