https://github.com/sujata-adhikari/machine-learning

Potato Disease Classification: Inspired by our kitchen garden, to detect potato disease using image of grown potato plant leaf.
https://github.com/sujata-adhikari/machine-learning

cnn-classification jyputer-notebook kaggle-dataset machine-learning opencv pandas python tensorflow

Last synced: 3 months ago
JSON representation

Potato Disease Classification: Inspired by our kitchen garden, to detect potato disease using image of grown potato plant leaf.

• Host: GitHub
• URL: https://github.com/sujata-adhikari/machine-learning
• Owner: sujata-adhikari
• Created: 2023-03-21T04:31:17.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-09-12T10:42:08.000Z (almost 3 years ago)
• Last Synced: 2025-01-14T01:50:01.948Z (over 1 year ago)
• Topics: cnn-classification, jyputer-notebook, kaggle-dataset, machine-learning, opencv, pandas, python, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 2.12 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
## Potato Disease Classification using CNN

A Machine learning Project to demonstrate image Classification.

This project helps us detect potoato disease when image of potato leave is given as input.

## Python libraries used

[Tensorflow](https://www.tensorflow.org/tutorials/images/classification)

[Matplotlib](https://matplotlib.org/)

[Numpy](https://numpy.org/)

[OS](https://docs.python.org/3/library/os.html)

## Installation

Install [Jyupter Notebook](https://docs.jupyter.org/en/latest/install.html)

 or use [Google Colab](https://colab.research.google.com/)

For DATA use [Kaggle](https://www.kaggle.com/datasets/arjuntejaswi/plant-village)

    

## Model Training 

    1.Download the data from kaggle.

    2.Keep folders related to Potatoes.

    3.Run Jupyter Notebook in Browser.

    jupyter notebook

    Open training/potato-disease-training.ipynb in Jupyter Notebook.

    In cell #2, update the path to dataset.

    Run all the Cells one by one.

    Copy the model generated and save it with the version number in the models folder.

## Contributing

Contributions are always welcome!

1. Report bugs: If you encounter any bugs, please let us know. Open up an issue and let us know the problem.

2. Contribute code: If you are a developer and want to contribute, follow the instructions below to get started.

3. Suggestions: If you don't want to code but have some awesome ideas, open up an issue explaining some updates or improvements you would like to see.

4. Documentation: If you see the need for some additional documentation, feel free to add some.