Plant pathology diagnosis: A computer vision application

Please read the project report available in main directory to find out more about the subject and how we approach it.

This project is based on a scientific publication [1] and a Kaggle competition [2] that took place from March 9 to May 26, 2020.

References

[1] Ranjita Thapa, Noah Snavely, Serge Belongie, and Awais Khan. The plant pathology 2020 challenge dataset to classify foliar disease of apples, 2020.

[2] Kaggle - plant pathology 2020 - Link : https://www.kaggle.com/c/plant-pathology-2020-fgvc7/overview.

How to use the code

The code associated with the project is organized as follows:

• Data augmentation (from images directly, locally): data_augmenting.py
• Creation of datasets (from previous images stored at the following git - https://gitlab.binets.fr/paul.calot-plaetevoet/plantpathology_2020.git): creatingDatasets.ipynb
• Development of architectures. The selected notebooks are as follows:
  • resnet50.ipynb
  • googlenet.ipynb
  • vgg16.ipynb
  • efnetb3.ipynb
  • processing_resnet50_definitive.ipynb
  • processing_resnet50_3classes.ipynb

A drive with the following datasets, in torch.dataset format, is also available at the following address: https://drive.google.com/drive/folders/1xTUMQhu-JbkTSriWoLJxmJXrj5jnrKym?usp=sharing

• Testing sets:

  • testing_set_3classes
  • testing_set_1400 (77-23% split)
  • testing_set_60 (60-40% split)

• Training sets:

  • dataset_8 (77-23% split)
  • dataset_4 (77-23% split)
  • dataset_6 (60-40% split)
  • dataset_3classes (77-23% split)
  • dataset_0 (77-23 split)

Whether on the git or on the drive, not all the datasets created are available: for reasons of available memory, we have only put the most relevant.