---
dataset_info:
- config_name: augmented
  features:
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: split
    dtype: string
  splits:
  - name: train
    num_bytes: 115320906
    num_examples: 1048
  download_size: 114631164
  dataset_size: 115320906
- config_name: raw
  features:
  - name: image
    dtype: image
  - name: mask
    dtype: image
  splits:
  - name: train
    num_bytes: 22801401
    num_examples: 248
  download_size: 22820806
  dataset_size: 22801401
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-segmentation
size_categories:
- 1K<n<10K
---
# Openear Projection Segmentation

This dataset provides real-world RGB images of maize ears in a field environment at Hongqi Base, Hainan, China, captured using a ground-based Raspberry Pi HQ camera system. Collected over a period from March 2025 to January 2026, it offers a longitudinal resource for semantic segmentation tasks in agricultural phenotyping under natural field conditions. The dataset contains Augmented and Raw versions.
The Augmented split contains 1,048 images with pixel-level mask annotations.  

The Raw split contains 248 images with pixel-level mask annotations.  


This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

The original train/test/val split has been preserved in the `split` column.

## Citation

```bibtex
@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). <i>Datasets for OpenEar model training</i> (Version 2). figshare. https://doi.org/10.6084/m9.figshare.26282563.v2

*This dataset was reformatted from its original format to match HuggingFace standards.*