Datasets:

Project-AgML
/

citrusuat_disease_classification

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': Citrus_leafminer
            '1': Fe
            '2': Greasy_spot
            '3': HLB
            '4': Healthy
            '5': Mg
            '6': Mn
            '7': 'N'
            '8': Red_scale
            '9': Red_scale_sequelae
            '10': Texas_mite
            '11': Zn
  splits:
    - name: train
      num_bytes: 1884058990
      num_examples: 953
  download_size: 1884130526
  dataset_size: 1884058990
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
task_categories:
  - image-classification
license: cc-by-4.0
size_categories:
  - n<1K

CitrusUAT Disease Classification

A dataset for disease classification of orange leaves. The dataset contains 953 images across 12 classes: Citrus_leafminer, Fe, Greasy_spot, HLB, Healthy, Mg, Mn, N, Red_scale, Red_scale_sequelae, Texas_mite, Zn.
Images per class:

Citrus_leafminer: 100
Fe: 100
Greasy_spot: 100
HLB: 43
Healthy: 100
Mg: 100
Mn: 30
N: 50
Red_scale: 30
Red_scale_sequelae: 100
Texas_mite: 100
Zn: 100

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

Citation

@article{gomez2024citrusuat,
  title={CitrusUAT: A dataset of orange Citrus sinensis leaves for abnormality detection using image analysis techniques},
  author={G{\'o}mez-Flores, Wilfrido and Garza-Salda{\~n}a, Juan Jos{\'e} and Varela-Fuentes, S{\'o}stenes Edmundo},
  journal={Data in brief},
  volume={52},
  pages={109908},
  year={2024},
  publisher={Elsevier}
}

Wilfrido Gómez Flores. (2023). CitrusUAT: A Dataset of Orange Citrus sinensis Leaves for Abnormality Detection Using Image Analysis Techniques [Data set]. In CitrusUAT: A dataset of orange Citrus sinensis leaves for abnormality detection using image analysis techniques (1.0, Vol. 52, p. 109908). Zenodo. https://doi.org/10.5281/zenodo.8294078