Datasets:
File size: 1,639 Bytes
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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 |