---
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., &amp; 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.*