Literature DB >> 28339793

Lameness assessment with automatic monitoring of activity in commercial broiler flocks.

A M Silvera1, T G Knowles2, A Butterworth2, D Berckmans3, E Vranken3,4, H J Blokhuis1.   

Abstract

The possibility of using automatic recordings of broiler chicken activity in commercial flocks to assess the birds΄ walking ability (lameness) was investigated. Data were collected from 5 commercial broiler farms in 4 European countries, using 16 flocks and 33 assessment occasions. Lameness was assessed using established gait scoring methods (Kestin et al., 1992; Welfare Quality®, 2009) and took place at 3, 4, and 5 wk of age. Gait score (GS) was used to assess the birds' walking ability, and automatic recordings of bird activity were collected using the eYeNamic™ camera system before, during, and after an assessor walked through the house. The variables used to predict the level of GS extracted from the camera system were: baseline activity, time from assessor leaving the house to resumption of baseline activity, average activity over that period, and Δ Amplitude (difference between highest activity peak after assessor left the house and baseline level). Age (<0.001) and Δ Amplitude (P = 0.0002) were significantly related to GS, with the gait getting poorer with increased age and Δ Amplitude decreasing with declining walking ability. Both measures are thus included in a predictive equation. The results demonstrate a potential method using image analysis techniques to realize an automated assessment of the level of lameness in commercial broiler flocks. This could be of use in future animal welfare assessment schemes.
© 2017 Poultry Science Association Inc.

Entities:  

Keywords:  gait score; image analysis; precision livestock farming; welfare

Mesh:

Year:  2017        PMID: 28339793     DOI: 10.3382/ps/pex023

Source DB:  PubMed          Journal:  Poult Sci        ISSN: 0032-5791            Impact factor:   3.352


  2 in total

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Authors:  Xiao Yang; Yang Zhao; George T Tabler
Journal:  Animals (Basel)       Date:  2020-06-26       Impact factor: 2.752

2.  Computer-Vision-Based Indexes for Analyzing Broiler Response to Rearing Environment: A Proof of Concept.

Authors:  Juliana Maria Massari; Daniella Jorge de Moura; Irenilza de Alencar Nääs; Danilo Florentino Pereira; Tatiane Branco
Journal:  Animals (Basel)       Date:  2022-03-28       Impact factor: 2.752

  2 in total

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