---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: objects
    struct:
    - name: bbox
      list:
        list: float64
    - name: categories
      list:
        class_label:
          names:
            '0': apple_blotch_acervulus
  - name: split
    dtype: string
  splits:
  - name: train
    num_bytes: 123508180
    num_examples: 927
  download_size: 123511401
  dataset_size: 123508180
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc0-1.0
task_categories:
- object-detection
size_categories:
- n<1K
---
# Apple Blotch Detection

Detached apple leaves photographed after artificial inoculation with Diplocarpon coronariae, the fungus behind apple blotch, as part of a resistance screening of apple genetic resources (551 varieties in 2022, 80 in 2023) at the Julius Kühn Institute in Dresden-Pillnitz, Germany. Leaves were imaged on a light table with a tripod-mounted Canon DSLR, and 768 x 768 crops of the leaf surface were annotated with bounding boxes around individual acervuli (the fungus's spore-producing spots) for training a YOLOv5 counting model. Crops without symptoms are kept as negatives with no boxes; the split column keeps the authors' train/val split. The dataset contains 927 images with 4,167 bounding box annotations across 1 category.

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

The original train/test/val split has been preserved in the `split` column.

## Citation

```bibtex
@article{reim2024yolo,
  title={YOLO-Based Phenotyping of Apple Blotch Disease (Diplocarpon coronariae) in Genetic Resources after Artificial Inoculation},
  author={Reim, Stefanie and Richter, Sophie and Leonhardt, Oskar and Maß, Virginia and Wöhner, Thomas Wolfgang},
  journal={Agronomy},
  volume={14},
  pages={1042},
  year={2024},
  publisher={MDPI}
}
```


*This dataset was reformatted from its original format to match HuggingFace standards.*