Towards automated chicken monitoring: dataset and machine learning methods for visual, noninvasive reidentification

Daria Kern*, Tobias Schiele, Ulrich Klauck, Winfred Ingabire

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

The chicken is the world’s most farmed animal. In this work, we introduce the Chicks4FreeID dataset, the first publicly available dataset focused on the reidentification of individual chickens. We begin by providing a comprehensive overview of the existing animal reidentification datasets. Next, we conduct closed-set reidentification experiments on the introduced dataset, using transformer-based feature extractors in combination with two different classifiers. We evaluate performance across domain transfer, supervised, and one-shot learning scenarios. The results demonstrate that transfer learning is particularly effective with limited data, and training from scratch is not necessarily advantageous even when sufficient data are available. Among the evaluated models, the vision transformer paired with a linear classifier achieves the highest performance, with a mean average precision of 97.0%, a top-1 accuracy of 95.1%, and a top-5 accuracy of 100.0%. Our evaluation suggests that the vision transformer architecture produces higher-quality embedding clusters than the Swin transformer architecture
Original languageEnglish
Article number1
Number of pages23
JournalAnimals
Volume15
Issue number1
Early online date24 Dec 2024
DOIs
Publication statusPublished - Jan 2025

Keywords

  • chicken; poultry; livestock; re-ID; individual identification; transformer; dataset; artificial intelligence; machine learning; computer vision
  • re-ID
  • chicken
  • poultry
  • computer vision
  • individual identification
  • livestock
  • transformer
  • machine learning
  • dataset
  • artificial intelligence

ASJC Scopus subject areas

  • Animal Science and Zoology
  • General Veterinary

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