Datasets:
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Abnormal_Developement
'1': Normal_Developement
- name: plot_id
dtype: string
splits:
- name: train
num_bytes: 652904358
num_examples: 6435
download_size: 642346771
dataset_size: 652904358
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
Openear Developmental Status Classification
This dataset provides real-world RGB images of maize ears collected in a field environment at Hongqi Base, Hainan, China, for developmental status classification. Images were captured using a ground-based Raspberry Pi HQ camera system with a Sony IMX477R sensor during the 2025-2026 growing season, offering high-resolution visual data for distinguishing between abnormal and normal developmental stages. The dataset contains 6,435 images across 2 classes: Abnormal_Developement, Normal_Developement.
Images per class:
- Abnormal_Developement: 2,152
- Normal_Developement: 4,283
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{fan2026openear,
title={OpenEar: an ultra-affordable, high-throughput, and accurate maize ear phenotyping system},
author={Fan, Shaoqi and Li, Guoji and Bahitwa, Revocatus and Jia, Zhiguo and Zhang, Hongwei and Shao, Jinghong and Yu, Qiuying and Chen, Xiaoran and Qian, Yiheng and Xu, Mingchi and Zhu, Linlin and Wang, Hai},
journal={Plant Methods},
volume={22},
pages={26},
year={2026},
publisher={BioMed Central}
}
Fan, S. (2025). Datasets for OpenEar model training (Version 2). figshare. https://doi.org/10.6084/m9.figshare.26282563.v2
This dataset was reformatted from its original format to match HuggingFace standards.