| --- |
| configs: |
| - config_name: augmented |
| data_files: |
| - split: train |
| path: augmented/train-* |
| - config_name: raw |
| data_dir: raw |
| default: true |
| license: cc-by-4.0 |
| task_categories: |
| - image-classification |
| size_categories: |
| - 1K<n<10K |
| dataset_info: |
| - config_name: augmented |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: |
| class_label: |
| names: |
| '0': Defect Dragon Fruit |
| '1': Fresh Dragon Fruit |
| splits: |
| - name: train |
| num_bytes: 643052945 |
| num_examples: 5000 |
| download_size: 466273651 |
| dataset_size: 643052945 |
| - config_name: raw |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: |
| class_label: |
| names: |
| '0': Defect Dragon Fruit |
| '1': Fresh Dragon Fruit |
| splits: |
| - name: train |
| num_bytes: 229208491 |
| num_examples: 1652 |
| download_size: 188877742 |
| dataset_size: 229208491 |
| --- |
| |
| # 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 |
