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--- |
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license: lgpl-3.0 |
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language: |
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- en |
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metrics: |
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- mae |
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- mse |
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- accuracy |
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tags: |
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- biology |
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- plant |
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- vitis |
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- downey mildew |
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- Plasmopara viticola |
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- OIV 452-1 |
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--- |
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# OIV Leaf Disc Phenotyping |
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Companion repository for the article |
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"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) |
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## Folder Structure |
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### checkpoints |
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Contains checkpoint files for leaf disc detector and OIV 452-1 scorer. |
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### data |
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Contains all datasets data in CSV format |
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### images |
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Contains all images in three different folders: |
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- leaf_discs contains full leaf discs |
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- leaf_patches contains extracted patches |
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- plates contains full plate images |
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### src |
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Contains source code under two formats: |
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- *.py files contain base functionality and classes |
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- *.ipynb files contain code to reproduce the article data |
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## Notebooks |
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### leaf_patch_extractor.ipynb |
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This notebook shows the process that goes from plate images to leaf patches |
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### leaf_patch_annotation.ipynb |
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Generates an user interface to annotate leaf patches |
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### leaf_patch_oiv_predictor.ipynb |
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Step by sterp tutorial to predict OIV 452-1 scores from extracted leaf patches |
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### leaf_patch_gen_diff.ipynb |
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Notebook that details the procedure to compare model predictions to human scores |
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### repo_manager.ipynb |
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Utility notebook to create this repository |
