---
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.*