---
configs:
- config_name: raw
  default: true
  data_dir: raw
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Defect Dragon Fruit
          '1': Fresh Dragon Fruit
- config_name: augmented
  data_dir: augmented
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Defect Dragon Fruit
          '1': Fresh Dragon Fruit
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
---

# Dragonfruit Quality Classification

A dataset for quality classification of dragonfruit. The dataset contains raw and augmented versions.  
The raw dataset contains 1,652 images.  
Images per class:
- Defect Dragon Fruit: 754
- Fresh Dragon Fruit: 898

The augmented dataset contains 5,000 images.  
Images per class:
- Defect Dragon Fruit: 3,000
- Fresh Dragon Fruit: 2,000


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

## Citation

```bibtex
@article{khatun2024comprehensive,
  title={A comprehensive dragon fruit image dataset for detecting the maturity and quality grading of dragon fruit},
  author={Khatun, Tania and Nirob, Md Asraful Sharker and Bishshash, Prayma and Akter, Morium and Uddin, Mohammad Shorif},
  journal={Data in Brief},
  volume={52},
  pages={109936},
  year={2024},
  publisher={Elsevier}
}
```

Khatun, Tania; Nirob, Md. Asraful Sharker ; Bishshash, Prayma  ; Uddin, Mohammad Shorif  (2023), “Dragon Fruit Maturity Detection and Quality Grading Dataset”, Mendeley Data, V1, doi: 10.17632/2jpzbx8tm6.1