Summary of included files

• The project proposal.
• The notebooks with the machine learning process and named after the models resnet50 and resnet34.
• The python web application.
• The project report.

To test the web app locally or deploy it on Render

This repo can be used as a starting point to deploy fast.ai models on Render.

The web app described here is up at https://plantdiseaseml.onrender.com, Test it out with images of plant leaves!

You can test your changes locally by installing Docker and using the following command:

docker build -t fastai-v3 . && docker run --rm -it -p 5000:5000 fastai-v3

The guide for production deployment to Render is at https://course.fast.ai/deployment_render.html.

Please use Render's fast.ai forum thread for questions and support.

Screenshots of the app:

• i
• ii

References

• Original crowdai dataset https://github.com/MichaelGerhard/PlantDiseaseData
• Resnet, https://neurohive.io/en/popular-networks/resnet/
• David Hughes paper, https://arxiv.org/ftp/arxiv/papers/1604/1604.03169.pdf
• Viridiana Romero Martinez's medium article https://medium.com/datadriveninvestor/creating-an-ai-app-that-detect-diseases-in-plants-using-facebooks-deep-learning-platform-pytorch-15faaeb6bec3
• https://towardsdatascience.com/create-and-deploy-an-image-classifier-using-fastai-and-render-in-15-mins-947f9de42d21