Datasets:
File size: 1,876 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: crop
dtype: string
- name: objects
struct:
- name: bbox
list:
list: float64
- name: categories
list:
class_label:
names:
'0': Blackbean
'1': Canola
'2': Corn
'3': Field Pea
'4': Flax
'5': Horseweed
'6': Kochia
'7': Lentil
'8': Ragweed
'9': Redroot Pigweed
'10': Soybean
'11': Sugar beet
'12': Waterhemp
splits:
- name: train
num_bytes: 7867132445
num_examples: 1120
download_size: 7802827920
dataset_size: 7867132445
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- object-detection
size_categories:
- 1K<n<10K
---
# Weed Crop Detection
A dataset for object detection of weeds and crops in fields. The dataset contains 1,120 images with 17,693 bounding box annotations across 13 categories.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
## Citation
```bibtex
@article{upadhyay2025weed,
title={Weed-crop dataset in precision agriculture: Resource for AI-based robotic weed control systems},
author={Upadhyay, Arjun and Mahecha, Maria Villamil and Mettler, Joseph and Howatt, Kirk and Aderholdt, William and Ostlie, Michael and Sun, Xin and others},
journal={Data in Brief},
volume={60},
pages={111486},
year={2025},
publisher={Elsevier}
}
```
Upadhyay, Arjun; G C, Sunil; Villamil Mahecha, Maria; Mettler, Joseph; Howatt, Kirk; Aderholdt, William; Ostlie, Michael; Sun, Xin (2025), “Weed-crop dataset in precision agriculture: Resource for AI-based robotic weed control systems”, Mendeley Data, V2, doi: 10.17632/mthv4ppwyw.2--> |