Datasets:
File size: 1,806 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': dead
'1': live
- name: split
dtype: string
splits:
- name: train
num_bytes: 270035028
num_examples: 3000
download_size: 270022669
dataset_size: 270035028
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
---
# Pea Seed Viability Classification
This dataset provides real RGB images of pea seeds collected in a laboratory environment in Beijing, China. The images were captured using a fixed-position MV-EM120C CCD camera under controlled conditions, offering a standardized resource for agricultural computer vision research on seed viability assessment. The dataset contains 3,000 images across 2 classes: dead, live.
Images per class:
- dead: 1,500
- live: 1,500
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{men2026rapid,
title={Rapid Seed Viability Detection Using Laser Speckle Weighted Generalized Difference with Improved Residual Networks},
author={Men, Sen and Zhang, Junhao and Liu, Xinhong and Sun, Tianyi and Liu, Wei},
journal={Agronomy},
volume={16},
pages={81},
year={2026},
publisher={MDPI}
}
```
The dataset itself can be cited as:
Sen Men. (2025). *Rapid Seed Viability Detection Using Laser Speckle Weighted Generalized Difference with Improved Residual Networks* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18041269
*This dataset was reformatted from its original format to match HuggingFace standards.*
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