Datasets:
Tasks:
Object Detection
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
< 1K
License:
Search is not available for this dataset
image imagewidth (px) 416 3.68k | objects dict |
|---|---|
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End of preview. Expand in Data Studio
CBDA — Cotton Ball Detection Dataset for Agriculture (Augmented)
A UAV-imagery dataset for cotton ball detection and counting on cotton crops. The dataset contains 180 images with 5,333 annotated cotton ball instances (single class: cotton_ball). Bounding boxes are in COCO format [x, y, width, height].
This augmented version extends the original CBDA collection with additional synthetic samples to improve model robustness across varying altitudes, lighting conditions, and crop densities.
| Split | Images | Annotations |
|---|---|---|
| train | 180 | 5,333 |
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Loading
from datasets import load_dataset
ds = load_dataset("Project-AgML/CBDA_Cotton_Ball_Detection_Augmented")
Citation
@article{amrani2023plant,
title={Plant Detection and Counting: Enhancing Precision Agriculture in UAV and General Scenes},
author={Amrani, Moussa and Sohel, Ferdous and Murray, Neil and Hazel, Susan},
journal={IEEE Access},
year={2023},
publisher={IEEE}
}
Amrani, Moussa; Sohel, Ferdous; Murray, Neil; Hazel, Susan (2023), "Plant Detection and Counting: Enhancing Precision Agriculture in UAV and General Scenes", IEEE Access
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