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Recherche transversale
(titres de publication, de périodique et noms de colloque inclus)
2021-06-30 - Article/Dans un journal avec peer-review - Anglais - 11 page(s)

Debauche Olivier , El Moulat Meryem, Mahmoudi Said , Bindelle Jérôme, Lebeau Frédéric, "Farm Animals’ Behaviors and Welfare Analysis with AI Algorithms: A Review" in Revue d'Intelligence Artificielle, 35, 3, 243-253

  • Edition : Lavoisier (France)
  • Codes CREF : Capteurs et périphériques (DI2563), Sciences agronomiques (DI3600), Informatique générale (DI1162)
  • Unités de recherche UMONS : Informatique, Logiciel et Intelligence artificielle (F114)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech), Institut NUMEDIART pour les Technologies des Arts Numériques (Numédiart)
Texte intégral :

Abstract(s) :

(Anglais) Numerous bibliographic reviews related to the use of AI for the behavioral detection of farm animals exist, but they only focus on a particular type of animal. We believe that some techniques were used for some animals that could also be used for other types of animals. The application and comparison of these techniques between animal species are rarely done. In this paper, we propose a review of machine learning approaches used for the detection of farm animals’ behaviors such as lameness, grazing, rumination, and so on. The originality of this paper is matched classification in the midst of sensors and algorithms used for each animal category. First, we highlight the most implemented approaches for different categories of animals (cows, sheep, goats, pigs, horses, and chickens) to inspire researchers interested to conduct investigation and employ the methods we have evaluated and the results we have obtained in this study. Second, we describe the current trends in terms of technological development and new paradigms that will impact the AI research. Finally, we critically analyze what is done and we draw new pathways of research to advance our understanding of animal’s behaviors.

Identifiants :
  • DOI : 10.18280/ria.350308

Mots-clés :
  • (Anglais) machine learning
  • (Anglais) livestock
  • (Anglais) chicken
  • (Anglais) cow
  • (Anglais) sheep
  • (Anglais) animal behavior
  • (Anglais) pig