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Project-AgML/citrus_fruit_leaf_disease_classification

Citrus Fruit Leaf Disease Classification A dataset for disease classification of citrus fruits and leaves. The dataset contains 759 images across 6 classes: black_spot, canker, greening, healthy, melanose, scab.Images per class: black_spot: 190 canker: 241 greening: 220 healthy: 80 melanose: 13 scab: 15 This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. Citation @article{rauf2019citrus, title={A citrus fruits and… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/citrus_fruit_leaf_disease_classification.

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Citrus Fruit Leaf Disease Classification

A dataset for disease classification of citrus fruits and leaves. The dataset contains 759 images across 6 classes: black_spot, canker, greening, healthy, melanose, scab. Images per class:

—black_spot: 190
—canker: 241
—greening: 220
—healthy: 80
—melanose: 13
—scab: 15

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

Citation

bibtex

@article{rauf2019citrus,
  title={A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning},
  author={Rauf, Hafiz Tayyab and Saleem, Basharat Ali and Lali, M Ikram Ullah and Khan, Muhammad Attique and Sharif, Muhammad and Bukhari, Syed Ahmad Chan},
  journal={Data in brief},
  volume={26},
  pages={104340},
  year={2019},
  publisher={Elsevier}
}

Rauf, Hafiz Tayyab; Saleem, Basharat ALi ; Lali, M. Ikram Ullah ; Khan, Muhammad Attique ; Sharif, Muhammad ; Bukhari, Syed Ahmad Chan (2019), “A Citrus Fruits and Leaves Dataset for Detection and Classification of Citrus Diseases through Machine Learning”, Mendeley Data, V2, doi: 10.17632/3f83gxmv57.2

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