Datasets:

Project-AgML
/

pomegranate_disease_classification

configs:
  - config_name: raw
    default: true
license: cc-by-4.0
task_categories:
  - image-classification
size_categories:
  - 1K<n<10K
dataset_info:
  config_name: raw
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': Colletotrichum spp
            '1': Ectomyelois ceratoniae
            '2': Healthy
            '3': Sunburn
  splits:
    - name: train
      num_bytes: 751472790
      num_examples: 2178
  download_size: 778518065
  dataset_size: 751472790

Pomegranate Disease Classification

A dataset for disease classification of pomegranate.
The dataset contains 2,178 images.
Images per class:

Colletotrichum spp: 571
Ectomyelois ceratoniae: 555
Healthy: 661
Sunburn: 391

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

Citation

@article{mohammed2025pomegranate,
  title={Pomegranate disease detection and classification dataset for deep learning applications: A case study from Halabja city},
  author={Mohammed, Bashdar Abdalrahman and Abdalla, Peshraw Ahmed and Aziz, Sirwan M and Hassan, Hiwa Omer},
  journal={Data in Brief},
  pages={112298},
  year={2025},
  publisher={Elsevier}
}

Mohammed, B. A., Abdalla, P. A., & Hassan, H. O. (2025). Halabja Pomegranate Fruit Disease Image Dataset. Zenodo. https://doi.org/10.5281/zenodo.15856012