{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:40:03Z","timestamp":1792543203280,"version":"4.5.0"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":53,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Data"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Broiler chicken production is a cornerstone of global food security, yet monitoring animals in commercial farms remains challenging due to high stocking densities and variable environmental conditions. Progress in automated monitoring is hindered by the scarcity of domain-specific, publicly available datasets. To address this gap, we present Poultry Images for Object detection (PIO), a dataset designed to support the development and evaluation of computer vision models for poultry farming. PIO comprises 1,487 manually annotated images containing 327,289 instances of broiler chickens, collected from both commercial and prototype poultry houses across different growth stages. The dataset reflects realistic conditions such as variations in morphology, lighting and bird density. Annotations were generated using the LabelImg tool, with bounding boxes normalized to image dimensions for compatibility with state-of-the-art detection frameworks. To illustrate its utility, three YOLOv10 variants were trained and evaluated on PIO, demonstrating its suitability for benchmarking object detection models in precision livestock farming contexts, as shown in figure 1.<\/jats:p>","DOI":"10.1038\/s41597-026-07114-5","type":"journal-article","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T04:17:22Z","timestamp":1775708242000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["PIO, A Large-Scale Dataset for Broiler Chicken Detection under Real Poultry Farming Conditions"],"prefix":"10.1038","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3008-3025","authenticated-orcid":false,"given":"Keyla","family":"Boniche","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7988-3293","authenticated-orcid":false,"given":"Edmanuel","family":"Cruz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1456-2253","authenticated-orcid":false,"given":"Jos\u00e9 Carlos","family":"Rangel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel","family":"Hidalgo-Rodr\u00edguez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7830-2661","authenticated-orcid":false,"given":"Francisco","family":"G\u00f3mez-Donoso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,9]]},"reference":[{"key":"7114_CR1","doi-asserted-by":"publisher","first-page":"1578318","DOI":"10.3389\/frobt.2025.1578318","volume":"12","author":"Y Lyu","year":"2025","unstructured":"Lyu, Y. et al. A review of robotic and automated systems in meat processing. Front. Robot. AI 12, 1578318, https:\/\/doi.org\/10.3389\/frobt.2025.1578318 (2025).","journal-title":"Front. Robot. AI"},{"key":"7114_CR2","doi-asserted-by":"publisher","first-page":"105281","DOI":"10.1016\/j.psj.2025.105281","volume":"104","author":"X He","year":"2025","unstructured":"He, X., Xue, H., Jia, Y., Xie, Z. & Li, L. Progress and trends of non-contact detection methods for poultry growth information: A review. Poult. Sci. 104, 105281 (2025).","journal-title":"Poult. Sci."},{"key":"7114_CR3","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.biosystemseng.2019.01.003","volume":"179","author":"X Zhuang","year":"2019","unstructured":"Zhuang, X. & Zhang, T. Detection of sick broilers by digital image processing and deep learning. Biosyst. Eng. 179, 106\u2013116 (2019).","journal-title":"Biosyst. Eng."},{"key":"7114_CR4","unstructured":"Ehsan, T. Z. & Mohtavipour, S. M. Broiler-Net: A deep convolutional framework for broiler behavior analysis in poultry houses. Preprint at arXiv https:\/\/arxiv.org\/abs\/2401.12176 (2024)."},{"key":"7114_CR5","doi-asserted-by":"publisher","first-page":"3390","DOI":"10.3390\/ani12233390","volume":"12","author":"Y Guo","year":"2022","unstructured":"Guo, Y., Aggrey, S. E., Wang, P., Oladeinde, A. & Chai, L. Monitoring behaviors of broiler chickens at different ages with deep learning. Animals 12, 3390 (2022).","journal-title":"Animals"},{"key":"7114_CR6","doi-asserted-by":"publisher","first-page":"945534","DOI":"10.3389\/fanim.2022.945534","volume":"3","author":"JAJ Van Der Eijk","year":"2022","unstructured":"Van Der Eijk, J. A. J. et al. Seeing is caring \u2013 automated assessment of resource use of broilers with computer vision techniques. Front. Anim. Sci. 3, 945534 (2022).","journal-title":"Front. Anim. 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Poultry Sci. 104, 104784 (2025).","journal-title":"Poultry Sci."},{"key":"7114_CR13","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.16686320","author":"K Boniche","year":"2025","unstructured":"Boniche, K. & Cruz, E. PIO, a large-scale dataset for broiler chicken detection under real poultry farming conditions DATASET v.1. Zenodo https:\/\/doi.org\/10.5281\/zenodo.16686320 (2025)."},{"key":"7114_CR14","unstructured":"Kale, P. D. Comparative analysis of image annotation tools: Label Img, VGG Annotator, Label Studio, and Roboflow. J. Emerg. Technol. Innov. Res. 11, https:\/\/www.jetir.org\/view?paper=JETIR2405D59 (2024)."},{"key":"7114_CR15","doi-asserted-by":"publisher","unstructured":"Redmon, J. et al. You Only Look Once: Unified, Real-Time Object Detection. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 779\u2013788, https:\/\/doi.org\/10.1109\/CVPR.2016.91 (2016).","DOI":"10.1109\/CVPR.2016.91"},{"key":"7114_CR16","doi-asserted-by":"publisher","unstructured":"Wang, A. et al. 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