| --- |
| 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.* |
|
|
