---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: mask
    dtype: image
  splits:
  - name: train
    num_bytes: 22742227
    num_examples: 224
  download_size: 22744687
  dataset_size: 22742227
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- image-segmentation
size_categories:
- n<1K
---

# Rice Seedling Segmentation

A dataset for semantic segmentation of Rice Seedling Segmentation. The dataset contains 224 images with pixel-level mask annotations.

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

## Citation

```bibtex
@Article{electronics9101602,
AUTHOR = {Khan, Abbas and Ilyas, Talha and Umraiz, Muhammad and Mannan, Zubaer Ibna and Kim, Hyongsuk},
TITLE = {CED-Net: Crops and Weeds Segmentation for Smart Farming Using a Small Cascaded Encoder-Decoder Architecture},
JOURNAL = {Electronics},
VOLUME = {9},
YEAR = {2020},
NUMBER = {10},
ARTICLE-NUMBER = {1602},
URL = {https://www.mdpi.com/2079-9292/9/10/1602},
ISSN = {2079-9292},
DOI = {10.3390/electronics9101602}
}
```

https://github.com/kabbas570/CED-Net-Crops-and-Weeds-Segmentation-for-Smart-Farming-Using-a-Small-Cascaded-Encoder-Decoder-Archi