---
configs:
- config_name: default
  default: true
  features:
  - name: image
    dtype: image
  - name: objects
    sequence:
    - name: bbox
      list: float32
    - name: categories
      class_label:
        names:
          '0': weed
          '1': cotton
license: cc-by-4.0
task_categories:
- object-detection
size_categories:
- n<1K
---

# Cotton Weed Detection

A dataset for weed detection in cotton fields. The dataset contains 262 images with 50,812 bounding box annotations across 2 categories, weed and cotton.

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

## Citation

```bibtex
@article{das2026uav,
  title={A UAV Image Dataset for Object Detection with Annotations Generated Using LabelImg and Roboflow},
  author={Das, Anindita and Subburaj, Vinitha Hannah and Yang, Yong and Bednarz, Craig W},
  journal={Data in Brief},
  pages={112483},
  year={2026},
  publisher={Elsevier}
}
```

Das, Anindita; Subburaj, Vinitha; Yang, Yong; Bednarz, Craig (2025), “A UAV Image Dataset for Object Detection with Annotations Generated Using LabelImg and Roboflow ”, Mendeley Data, V1, doi: 10.17632/sx2tphzvcw.1