README.md

---
license: lgpl-3.0
language:
- en
metrics:
- mae
- mse
- accuracy
tags:
- biology
- plant
- vitis
- downey mildew
- Plasmopara viticola
- OIV 452-1
---
# 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](https://link.springer.com/article/10.1186/s13007-024-01220-4?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20240613&utm_content=10.1186/s13007-024-01220-4)

## 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