metadata

license: lgpl-3.0
language:
  - en
metrics:
  - mae
  - mse
  - accuracy
tags:
  - biology
  - plant
  - vitis
  - downey mildew
  - Plasmopara viticola
  - OIV 452-1
base_model: microsoft/swin-tiny-patch4-window7-224

OIV Leaf Disc Phenotyping

Companion repository for the article "Phenotyping grapevine resistance to downy mildew: deep learning as a promising tool to assess sporulation and necrosis" found Here

Folder Structure

checkpoints

Contains checkpoint files for leaf disc detector and OIV 452-1 scorer.

data

Contains all datasets data in CSV format

images

Contains all images in three different folders:

leaf_discs contains full leaf discs
leaf_patches contains extracted patches
plates contains full plate images

src

Contains source code under two formats:

*.py files contain base functionality and classes
*.ipynb files contain code to reproduce the article data

Notebooks

leaf_patch_extractor.ipynb

This notebook shows the process that goes from plate images to leaf patches

leaf_patch_annotation.ipynb

Generates an user interface to annotate leaf patches

leaf_patch_oiv_predictor.ipynb

Step by sterp tutorial to predict OIV 452-1 scores from extracted leaf patches

leaf_patch_gen_diff.ipynb

Notebook that details the procedure to compare model predictions to human scores

repo_manager.ipynb

Utility notebook to create this repository