Datasets:
| 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.* | |