| --- |
| dataset_info: |
| - config_name: augmented |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: |
| class_label: |
| names: |
| '0': broken_high |
| '1': broken_low |
| '2': discoloured_high |
| '3': discoloured_low |
| '4': healthy_large |
| '5': healthy_medium |
| '6': insect_infected |
| '7': unhulled |
| - name: augmentation_set |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 21884122969 |
| num_examples: 49896 |
| download_size: 21886330426 |
| dataset_size: 21884122969 |
| - config_name: raw |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: |
| class_label: |
| names: |
| '0': broken_high |
| '1': broken_low |
| '2': discoloured_high |
| '3': discoloured_low |
| '4': healthy_large |
| '5': healthy_medium |
| '6': insect_infected |
| '7': unhulled |
| splits: |
| - name: train |
| num_bytes: 5056745325 |
| num_examples: 16632 |
| download_size: 5057080851 |
| dataset_size: 5056745325 |
| configs: |
| - config_name: augmented |
| data_files: |
| - split: train |
| path: augmented/train-* |
| - config_name: raw |
| data_files: |
| - split: train |
| path: raw/train-* |
| default: true |
| license: cc-by-4.0 |
| task_categories: |
| - image-classification |
| size_categories: |
| - 10K<n<100K |
| --- |
| # Wild Rice Kernel Quality Classification |
|
|
| RGB images of single Canadian wild rice (Zizania palustris) kernels photographed under controlled laboratory conditions at the University of Manitoba with a Canon EOS R100 and cropped to 512x512 patches. Kernels are graded into eight quality classes: Healthy-Large, Healthy-Medium, Discoloured-Low, Discoloured-High, Broken-Low, Broken-High, Insect-damaged and Unhulled. The raw config holds the 16,632 original images and the augmented config the record's 49,896 augmented copies (three augmentation sets, A to C, per original). The paired VNIR hyperspectral data are in a separate record. The dataset contains raw and augmented versions. |
| The raw dataset contains 16,632 images. |
| Images per class: |
| - broken_high: 2,124 |
| - broken_low: 2,124 |
| - discoloured_high: 2,052 |
| - discoloured_low: 2,016 |
| - healthy_large: 2,088 |
| - healthy_medium: 2,052 |
| - insect_infected: 2,160 |
| - unhulled: 2,016 |
| |
| The augmented dataset contains 49,896 images. |
| Images per class: |
| - broken_high: 6,372 |
| - broken_low: 6,372 |
| - discoloured_high: 6,156 |
| - discoloured_low: 6,048 |
| - healthy_large: 6,264 |
| - healthy_medium: 6,156 |
| - insect_infected: 6,480 |
| - unhulled: 6,048 |
|
|
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{sikiru2026multimodal, |
| title={A multimodal imaging dataset for quality grading of Canadian wild rice kernels using RGB and VNIR hyperspectral data}, |
| author={Sikiru, Yinka and Erkinbaev, Chyngyz}, |
| journal={Data in Brief}, |
| volume={65}, |
| pages={112601}, |
| year={2026}, |
| publisher={Elsevier} |
| } |
| ``` |
|
|
|
|
| The dataset itself can be cited as: |
|
|
| Sikiru, Y., & Erkinbaev, C. (2026). *RGB dataset of canadian wild rice kernels* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18346354 |
|
|
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
|
|
