---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: objects
    struct:
    - name: bbox
      list:
        list: float64
    - name: categories
      list:
        class_label:
          names:
            '0': germinated
            '1': not_germinated
  - name: crop
    dtype: string
  - name: dish_series
    dtype: string
  - name: time_step
    dtype: int32
  - name: split
    dtype: string
  splits:
  - name: train
    num_bytes: 836765157
    num_examples: 23797
  download_size: 826592593
  dataset_size: 836765157
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- object-detection
size_categories:
- 10K<n<100K
---
# Seed Germination Detection

Time-series images of germination experiments for maize (Zea mays), rye (Secale cereale) and pearl millet (Pennisetum glaucum): 2,449 seeds on black cloth in petri dishes, photographed repeatedly, giving 23,797 images (624x624, one dish each). Every seed is boxed and labelled germinated (radicle emerged from the seed coat) or not germinated. The crop, dish series, time step and the authors' train/validation/test split (by dish series) are kept as columns. The dataset contains 23,797 images with 235,933 bounding box annotations across 2 categories.

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{genze2020accurate,
  title={Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops},
  author={Genze, Nikita and Bharti, Richa and Grieb, Michael and Schultheiss, Sebastian J. and Grimm, Dominik G.},
  journal={Plant Methods},
  volume={16},
  pages={157},
  year={2020},
  publisher={BioMed Central}
}
```


The dataset itself can be cited as:

Nikita Genze. (2023). *Data for: Accurate Machine Learning-Based Germination Detection, Prediction and Quality Assessment of Various Seed Cultivars* [Dataset]. Mendeley. https://doi.org/10.17632/4WKT6THGP6.3


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