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PIO, A Large-Scale Dataset for Broiler Chicken Detection under Real Poultry Farming Conditions.

Keyla Boniche1, Edmanuel Cruz2,3, José Carlos Rangel4,5

  • 1Facultad de Ingeniería Mecánica, Universidad Tecnológica de Panamá, Panamá City, 0819-07289, Panamá. keyla.boniche@utp.ac.pa.

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|April 9, 2026
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Summary
This summary is machine-generated.

A new dataset, Poultry Images for Object detection (PIO), aids automated monitoring in broiler chicken farming. This resource supports computer vision model development for precision livestock farming, enhancing food security.

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Animal Science

Background:

  • Automated monitoring of broiler chickens is crucial for food security but faces challenges due to high stocking densities and environmental variability.
  • A lack of domain-specific, publicly available datasets hinders progress in automated monitoring technologies for poultry farming.

Purpose of the Study:

  • To introduce the Poultry Images for Object detection (PIO) dataset, designed to facilitate the development and evaluation of computer vision models for broiler chicken farming.
  • To provide a valuable resource for researchers and developers working on precision livestock farming solutions.

Main Methods:

  • The PIO dataset consists of 1,487 manually annotated images featuring 327,289 broiler chicken instances.
  • Images were captured in commercial and prototype poultry houses, reflecting realistic conditions like varying morphology, lighting, and bird density.
  • Annotations were created using LabelImg, with normalized bounding boxes for compatibility with object detection frameworks.

Main Results:

  • Three YOLOv10 variants were trained and evaluated using the PIO dataset.
  • The dataset's utility was demonstrated for benchmarking object detection models in precision livestock farming.

Conclusions:

  • The PIO dataset is a significant contribution towards advancing automated monitoring systems in the poultry industry.
  • It enables the development and validation of computer vision models essential for improving efficiency and welfare in broiler chicken production.