Dianyo/poultry-health

Poultry Health Fecal Image Dataset (Binary Classification)

Dataset updated to whole dataset instead of part of them on March 1, 2026

Dataset Summary

This repository provides a Hugging Face-compatible version of the Poultry Birds Poo Imagery Dataset for Health Status Prediction.

The goal of this Hugging Face port is to make the dataset instantly accessible for machine learning pipelines, particularly for PyTorch, Vision Transformers (ViTs), and Federated Learning (FL) setups. By hosting it here, researchers can bypass manual downloading and preprocessing, and load the data directly via the datasets library.

This is a binary classification dataset containing approximately 14,618 images of poultry droppings captured across various farms in Nigeria using mobile devices. The images are categorized into two distinct classes:

• Healthy
• Unhealthy

Because these images were taken at different times of the day (morning, afternoon, night) with varying light intensities and real-world farm backgrounds, this dataset is highly valuable for training robust computer vision models and testing decentralized AI architectures on noisy, authentic agricultural data.

How to Use

You can easily load this dataset using the Hugging Face datasets library:

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("Dianyo/poultry-health")

Original Source & Attribution

I did not collect these images; I have simply organized and formatted them for the Hugging Face ecosystem to enable easier pipeline integration. All credit for the data collection goes to the original authors.

• Original Dataset: Poultry Birds Poo Imagery Dataset for Health Status Prediction: A Case of South-West Nigeria
• Authors: Adebayo, Segun; Aworinde, Halleluyah; Akinwunmi, Akinwale; Alabi, Olufemi; Ayandiji, Adebamiji; Sakpere, Aderonke (2023)
• Original Host: Mendeley Data

Citation

If you use this Hugging Face dataset in your research or machine learning pipelines, please cite both the original authors who collected the images and this Hugging Face repository.

1. Cite the original data collection:

@dataset{adebayo2023poultry,
author = {Adebayo, Segun and Aworinde, Halleluyah and Akinwunmi, Akinwale and Alabi, Olufemi and Ayandiji, Adebamiji and Sakpere, Aderonke},
title = {Poultry Birds Poo Imagery Dataset for Health Status Prediction: A Case of South-West Nigeria},
year = {2023},
publisher = {Mendeley Data},
version = {1},
doi = {10.17632/8pnbzpt2k9.1},
url = {https://data.mendeley.com/datasets/8pnbzpt2k9/1}
}

2. Cite this Hugging Face dataset port:

@misc{dianyo2026poultryhealth,
author = {Tien-Yu Chi},
title = {Hugging Face Dataset Port: Poultry Health Fecal Images for Binary Classification},
year = {2026},
url = {https://huggingface.co/datasets/Dianyo/poultry-health}
}