---
configs:
- config_name: default
  default: true
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': bruised
          '1': cracked
          '2': rotten
          '3': spotted
          '4': unaffected
          '5': unripe
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
---

# African Plum Grading Classification

A dataset for grade classification of plums. The dataset contains 4,507 images across 6 classes: bruised, cracked, rotten, spotted, unaffected, unripe.
Images per class:
- bruised: 319
- cracked: 162
- rotten: 720
- spotted: 759
- unaffected: 1,721
- unripe: 826

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

## Citation

```bibtex
@article{fadja2025dataset,
  title={A dataset of annotated African plum images from Cameroon for AI-based quality assessment},
  author={Fadja, Arnaud Nguembang and Tagni, Armel Gabin Fameni and Che, Sain Rigobert and Atemkeng, Marcellin},
  journal={Data in Brief},
  volume={59},
  pages={111351},
  year={2025},
  publisher={Elsevier}
}
```

Arnaud Nguembang Fadja, and Armel Gabin Fameni Tagni. (2024). African Plums Dataset [Dataset]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/9694239