Datasets:
File size: 1,709 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: objects
struct:
- name: bbox
list:
list: float64
- name: categories
list:
class_label:
names:
'0': class0
- name: split
dtype: string
splits:
- name: train
num_bytes: 1224391
num_examples: 40
download_size: 1225894
dataset_size: 1224391
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- object-detection
size_categories:
- n<1K
---
# Cottonsim Detection
This dataset provides synthetic images of cotton plants in agricultural settings for object detection in crop monitoring. Captured using RealSense RGB-D cameras on a ground-based platform, the imagery simulates field conditions to support vision-guided agricultural robotics development. The dataset contains 40 images with 1,093 bounding box annotations across 1 category.
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{thayananthan2025cottonsim,
title={CottonSim: A vision-guided autonomous robotic system for cotton harvesting in Gazebo simulation},
author={Thayananthan, Thevathayarajh and Zhang, Xin and Huang, Yanbo and Chen, Jingdao and Wijewardane, Nuwan K. and Martins, Vitor S. and Chesser, Gary D. and Goodin, Christopher T.},
journal={Computers and Electronics in Agriculture},
volume={239},
pages={110963},
year={2025},
publisher={Elsevier}
}
```
*This dataset was reformatted from its original format to match HuggingFace standards.*
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