Datasets:

Project-AgML
/

GYMNSA_pear_rust_detection

configs:
  - config_name: default
    default: true
    features:
      - name: image
        dtype: image
      - name: objects
        sequence:
          - name: bbox
            list: float32
          - name: categories
            dtype: int32
license: cc-by-4.0
task_categories:
  - object-detection
size_categories:
  - n<1K

GYMNSA Pear Rust Detection

A dataset for object detection of pear rust on leaevs. The dataset contains 746 images with 16,251 bounding box annotations across 1 category.

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

Citation

@article{mass2025annotated,
  title={Annotated image dataset with different stages of European pear rust for UAV-based automated symptom detection in orchards},
  author={Ma{\ss}, Virginia and Alirezazadeh, Pendar and Seidl-Schulz, Johannes and Leipnitz, Matthias and Fritzsche, Eric and Ibraheem, Rasheed Ali Adam and Geyer, Martin and Pflanz, Michael and Reim, Stefanie},
  journal={Data in Brief},
  volume={58},
  pages={111271},
  year={2025},
  publisher={Elsevier}
}

Maß, Virginia; Alirezazadeh, Pendar; Seidl-Schulz, Johannes; Leipnitz, Matthias; Fritzsche, Eric; Ibraheem, Rasheed Ali Adam; Geyer, Martin; Pflanz, Michael; Reim, Stefanie (2024), “GYMNSA dataset”, Mendeley Data, V1, doi: 10.17632/44kjgc4gkc.1