Tomato-Plant-Disease-Detector

Project is live at : Tomato-Plant-Disease-Detector

Quick Offline Setup :

• method 1 (using Docker)

• Open cmd in local and run all below commands..

  git clone https://github.com/BhavyBansal24/Tomato-Plant-Disease-Detector.git
  docker build -t tomato_app .
  docker run -dp 8501:8501 --name Tomantina tomato_app
  timeout 2 /nobreak && start http://localhost:8501
  
  

• Method 2 : (using Conda venv)

• Clone this repo

• Open cmd in Clone folder

• Create a conda environment on your device using commands below on cmd,

  conda create -n test_env python=3.9
  conda activate test_env
  

• Install require libraries

  pip install requirements.txt
  

• Run the application

  streamlit run app.py
  

How to use Tomato-Plant-Disease-Detector web-app:

• Click here
• Select type of Input ['Upload Image', 'Take A Shot', 'Live Camera (Experimental)']

Upload Image

• Click on Browse files & Upload a Image file, Model will classify your uploaded Image from the known tomato disease classes.
• After uploading completes, you can see the prediction on right side as shown below

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Take A Shot

• Take a shot of the tomato plant leaf by clicking Take Photo on right side of the application.

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Live Camera (Experimental)

• As of now Streamlit is not supporting to use camera directly from API.
• Here is an way to access the Live Camera feature on your offline device :
  • Clone this repo
  • Open cmd in Clone folder
  • Create a conda environment on your device using commands below on cmd,

  conda create -n test_env python=3.9
  conda activate test_env
  pip install requirements.txt
  streamlit run app.py
  

  • Note : you may be asked for Email to start streamlit (proceed anyways)
  • It will redirect you to your default browser and open application offline
  • here you may use the Live Camera (Experimental) feature without any error.

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Model Details :

• Sequential model
• Training of 10 Epoch

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Model Results :

• Model Accuracy and Loss
  • 

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Model Evaluation

• Classification Report
  • 
• Confusion Matrix
  • 

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Dataset used for Training model:

• Dataset is taken from kaggle and link for dataset is here
• This dataset contains images of 256 X 256 size & RGB colored of different plant species.
• For this project I have choosen tomoto plant with 10 classes of diseases namely :
  • Bacterial Spot
  • Early Blight
  • Healthy
  • Late Blight
  • Leaf Mold
  • Septoria Leaf Spot
  • Spider Mites Two-Spotted Spider Mite
  • Target Spot
  • Tomato Mosaic Virus
  • Tomato Yellow Leaf Curl Virus

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Libraries and framework used in project :

• Pandas
• Numpy
• Seaborn
• Sklearn
• Matplotlib
• streamlit
• Tensorflow
• Keras
• OpenCV

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Do check my Kaggle (ipynb)Notebook:

• Link to my kaggle notebook is here
• Do upvote my notebook, Hope you like it.
• and do Not forgot to check my other notebooks on kaggle as well

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Do 🌟 this Github Repository, Hope you have loved my work

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Moreover don't forget to follow me on :

• github
• Kaggle
• Linkedin
• Twitter
• Instagram

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Portfolio Website :

• Hope you love to know more about me, check my Portfolio Website here