Datasets:
File size: 1,853 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': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
- name: split
dtype: string
splits:
- name: train
num_bytes: 1109729347
num_examples: 4156
download_size: 1109801762
dataset_size: 1109729347
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
---
# Wheat Disease Detection
This dataset provides real RGB images of wheat crops in field environments from Yuanyang, Henan Province, China, captured during the 2023 growing season. Collected using smartphone and DSLR cameras under natural field conditions, it offers diverse observations for object detection of wheat diseases in agricultural settings. The dataset contains 4,156 images with 5,845 bounding box annotations across 7 categories.
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{qian2025gdfc,
title={GDFC-YOLO: An Efficient Perception Detection Model for Precise Wheat Disease Recognition},
author={Qian, Jiawei and Dai, Chenxu and Ji, Zhanlin and Liu, Jinyun},
journal={Agriculture},
volume={15},
pages={1526},
year={2025},
publisher={MDPI}
}
```
The dataset itself can be cited as:
Ma, C. (2024). *Wheat Dataset* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.15621359
*This dataset was reformatted from its original format to match HuggingFace standards.*
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