---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Alternaria_Boltch
          '1': Brown_Spot
          '2': Grey_spot
          '3': Mosaic
          '4': Rust
  splits:
  - name: train
    num_bytes: 574217137
    num_examples: 26377
  download_size: 886186963
  dataset_size: 574217137
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 10K<n<100K
---
# Pepper Disease Pest Classification

The dataset comprises real RGB images of pepper plants in field environments, collected over two years (2022-2023) in Shouguang City, China. Captured using a fixed platform setup, the images provide consistent visual data for computer vision research focused on agricultural disease detection in pepper crops. The dataset contains 26,377 images across 5 classes: Alternaria_Boltch, Brown_Spot, Grey_spot, Mosaic, Rust.  
Images per class:
- Alternaria_Boltch: 5,343
- Brown_Spot: 5,655
- Grey_spot: 4,810
- Mosaic: 4,875
- Rust: 5,694

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

## Citation

```bibtex
@article{wang2025advanced,
  title={An advanced deep learning method for pepper diseases and pests detection},
  author={Wang, Xuewei and Liu, Jun and Chen, Qian},
  journal={Plant Methods},
  volume={21},
  pages={70},
  year={2025},
  publisher={BioMed Central}
}
```

liu, jun (2024), “vegetable disease”, Mendeley Data, V1, doi: 10.17632/tg3z7xxkdb.1

*This dataset was reformatted from its original format to match HuggingFace standards.*