Tomato Leaf Classification with Dockerization and Azure Deployment

This is a project on Tomato leaf classification with dockerization and deployment in Azure.

Our classes are:

• Tomato___Bacterial_spot
• Tomato___Early_blight
• Tomato___healthy
• Tomato___Late_blight
• Tomato___Leaf_Mold
• Tomato___Septoria_leaf_spot
• Tomato___Spider_mites Two-spotted_spider_mite
• Tomato___Target_Spot
• Tomato___Tomato_mosaic_virus
• Tomato___Tomato_Yellow_Leaf_Curl_Virus

How to set up

1. Clone the repository:

   git clone https://github.com/your-repo/tomato-leaf-classification.git
   

2. Install the requirements using the requirements.txt file:

   pip install -r requirements.txt
   

3. Create the following folders:

   • Datasets: This folder will contain your dataset.
   • ML: This folder is where you should add your ML model.

4. Download the dataset from Tomato Leaf Disease Detection on Kaggle and place it in the Datasets folder.

5. Correct the paths to the tomato images Dataset and train the Model using the code in ML/train.ipynb.

6. Add your ML model to the ML folder and change the paths in the code to point to your model.

7. Navigate to the main directory and run the following command to start the application:

   uvicorn BACKEND.main:app
   

Now you should be able to access the API on your web browser on:

   http://127.0.0.1:8000/docs

8. If you want to dockerize the the application download and install docker in your desktop run the following command in the terminal.

   docker build -t <your_docker_image_name> .
   

9. Now you can create the docker container manually through the docker desktop or run the command in the terminal to run the container:

   docker run -p 8000:8000 <your_docker_image_name>

10. To publish on the docker hub.

    First Create a new docker tag using the following command:

     docker tag <your_docker_image_name> <your_username>/<your_docker_image_name>:v1.0
    

    Then push the tag to the docker hub:

     docker push <your_docker_image_name>/<your_docker_image_name>:v1.0
    

11. Now your docker image is ready to be shared and hosted in different platforms

The pictures below showcase the results of the project when hosted on Azure.

Result Images