Datasets:
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
@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.