Datasets:
File size: 1,698 Bytes
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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.*
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