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
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
- 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
- streamlit run app.py
-
About
-
Disease Predection
-
Disease Classification
-
Treatement Page
- opencv-contrib-python-headless
- tensorflow-cpu
- streamlit
- numpy
- pandas
- pillow
- keras
- matplotlib