Dataset Creation for the Artificial Intelligence-based Video Analysis of Yellow Hungarian Chicken Flocks

Authors

  • Éva Rampasek Multidisciplinary Doctoral School of Wittmann Antal Crop-, Animal- and Food Sciences; Faculty of Albert Kázmér Mosonmagyaróvár Széchenyi István University
  • Csaba Hajdu Department of Informatics, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University https://orcid.org/0000-0002-1058-7525
  • Boldizsár Tüű Szabó Department of Informatics, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University https://orcid.org/0000-0003-3217-9204
  • Károly Tempfli Department of Animal Science, Faculty of Albert Kázmér Mosonmagyaróvár Széchenyi István University https://orcid.org/0000-0003-3119-8294
  • László Környei Department of Mathematics, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University https://orcid.org/0000-0002-0782-2985

Keywords:

Hens, YOLO, precision livestock, deep learning, object tracking, object detection, artificial intelligence, computer vision

Abstract

A Precision Livestock Farming (PLF) system offers real-time insights into animal welfare and health, increasing production efficiency. Computer vision in the poultry industry enables automated monitoring of animals, providing a wealth of information without the need for human resources. The adoption of the “End the Cage Age” initiative by the European Commission will phase out caging of farm animals expectedly from 2027, impacting the laying hen sector, especially in Hungary where a significant portion of hens are currently raised in cage systems that are being replaced with cage-free systems requiring continuous monitoring of social behaviour. The initial step in implementing machine vision involves identifying hens and determining the number of individuals visible in images. The study involved automatic monitoring of various aspects such as behaviour, welfare, and health of Yellow Hungarian chickens at a breeding farm in Mosonmagyaróvár. Video recordings captured the activities of 49 hens in a controlled environment, focusing on detecting individual bird movements and predicting behaviours using advanced image processing techniques. The researchers utilized open bird datasets and annotation tools to create a specialized dataset for chicken behaviour analysis, showing promising results for future enhancements in detection and prediction capabilities.

Author Biographies

  • Éva Rampasek, Multidisciplinary Doctoral School of Wittmann Antal Crop-, Animal- and Food Sciences; Faculty of Albert Kázmér Mosonmagyaróvár Széchenyi István University

    correspondence
    rampasekeva@yahoo.com

  • Csaba Hajdu, Department of Informatics, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University

    correspondence
    hajdu.csaba@ga.sze.hu

  • Boldizsár Tüű Szabó, Department of Informatics, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University

    tuu.szabo.boldizsar@sze.hu

  • Károly Tempfli, Department of Animal Science, Faculty of Albert Kázmér Mosonmagyaróvár Széchenyi István University

    tempfli.karoly@sze.hu

  • László Környei, Department of Mathematics, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University

    laszlo.kornyei@math.sze.hu

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Published

2024-06-28

Issue

Vol. 28 No. Suppl. 2 (2024): 65th Georgikon Days Scientific Conference

Section

Articles

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Copyright (c) 2024 Éva Rampasek, Csaba Hajdu, Boldizsár Tüű Szabó, Károly Tempfli, László Környei

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