Project-AgML/pea_seed_viability_classification

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

@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.