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Project-AgML/citrusuat_disease_classification

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… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/citrusuat_disease_classification.

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Dataset Card

CitrusUAT Disease Classification

A dataset for disease classification of orange leaves. The dataset contains 953 images across 12 classes: Citrusleafminer, Fe, Greasyspot, HLB, Healthy, Mg, Mn, N, Redscale, Redscalesequelae, Texasmite, 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
—Redscalesequelae: 100
—Texas_mite: 100
—Zn: 100

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

Citation

bibtex

@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