dataset_info:
features:
- name: image
dtype: image
- name: length_mm
dtype: float64
- name: area_mm2
dtype: float64
splits:
- name: train
num_bytes: 4350284008
num_examples: 891
download_size: 4350390038
dataset_size: 4350284008
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- other
size_categories:
- n<1K
Deepcanola Br9
This dataset provides ground truth annotations for crop segmentation of canola (Brassica napus) in agricultural field environments. It features a mixed collection of real and synthetic RGB imagery captured under typical outdoor farming conditions. The resource supports computer vision research focused on phenotyping and segmentation tasks for canola crops in real-world agricultural settings. The dataset contains 891 images, each paired with the following ground-truth measurement(s): area_mm2, length_mm.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{vanvliet2025deepcanola,
title={DeepCanola: Phenotyping brassica pods using semi-synthetic data and active learning},
author={van Vliet, Larissa J.J. and Atkins, Kieran and Kurup, Smita and Siles, Laura and Hepworth, Jo and Corke, Fiona M.K. and Doonan, John H. and Lu, Chuan},
journal={Computers and Electronics in Agriculture},
volume={237},
pages={110470},
year={2025},
publisher={Elsevier}
}
Atkins, K., van Vliet, L., Doonan, J., & Lu, C. (2024). DeepCanola: Phenotyping Brassica Pods Using Semi-Synthetic Data and Active Learning [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.14235543
This dataset was reformatted from its original format to match HuggingFace standards.