---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: plant_id
    dtype: string
  - name: split
    dtype: string
  splits:
  - name: train
    num_bytes: 4998089348
    num_examples: 4764
  download_size: 4998717861
  dataset_size: 4998089348
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-segmentation
size_categories:
- 1K<n<10K
---
# Barley Disease Segmentation

The dataset provides real RGB images of barley plants exhibiting disease symptoms, captured in a laboratory environment across multiple sites in France and Germany. Images were collected using a fixed platform during the 2024-2025 period, offering a standardized resource for semantic segmentation models targeting barley disease identification. The dataset contains 4,764 images with pixel-level mask annotations.

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{martello2026deep,
  title={Deep learning–based identification of visually similar foliar diseases in field-grown barley},
  author={Martello, Sofia and Genze, Nikita and Grimm, Dominik G.},
  journal={Plant Methods},
  volume={22},
  pages={41},
  year={2026},
  publisher={BioMed Central}
}
```

Martello, Sofia; Genze, Nikita; Grimm, Dominik (2026), “Data for: Deep Learning–Based Identification of Visually Similar Foliar Diseases in Field-Grown Barley”, Mendeley Data, V1, doi: 10.17632/4ny92p2r8f.1

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