Literature DB >> 31121361

Computer vision and remote sensing to assess physiological responses of cattle to pre-slaughter stress, and its impact on beef quality: A review.

Maria Jorquera-Chavez1, Sigfredo Fuentes2, Frank R Dunshea2, Ellen C Jongman3, Robyn D Warner2.   

Abstract

Pre-slaughter stress is well-known to affect meat quality of beef carcasses and methods have been developed to assess this stress. However, development of more practical and less invasive methods are required in order to assess the response of cattle to pre-slaughter stressors, which will potentially also assist with the prediction of beef quality. This review outlines the importance of pre-slaughter stress as well as existing and emerging technologies for quantification of the pre-slaughter stress. The review includes; i) indicators of meat quality and how they are affected by pre-slaughter stress in cattle, ii) contact techniques that have been commonly used to measure stress indicators in animals, iii) remotely sensed imagery techniques recently used as non-invasive methods to monitor physiological and behavioural parameters and iv) potential implementation of remotely sensed imagery data to perform contactless assessment of physiological measurements, which could be related to the pre-slaughter stress, as well as to the indicators of beef quality. Relevance to industry, conclusions and recommendations for research are included.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Beef quality; Computer vision technology; Contactless monitoring; Physiological parameters; Pre-slaughter stress

Mesh:

Year:  2019        PMID: 31121361     DOI: 10.1016/j.meatsci.2019.05.007

Source DB:  PubMed          Journal:  Meat Sci        ISSN: 0309-1740            Impact factor:   5.209


  3 in total

1.  Influence of beef genotypes on animal performance, carcass traits, meat quality, and sensory characteristics in grazing or feedlot-finished steers.

Authors:  Isabella C F Maciel; J P Schweihofer; J I Fenton; J Hodbod; M G S McKendree; K Cassida; J E Rowntree
Journal:  Transl Anim Sci       Date:  2021-09-21

2.  Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras.

Authors:  Sigfredo Fuentes; Claudia Gonzalez Viejo; Surinder S Chauhan; Aleena Joy; Eden Tongson; Frank R Dunshea
Journal:  Sensors (Basel)       Date:  2020-11-06       Impact factor: 3.576

Review 3.  Affective State Recognition in Livestock-Artificial Intelligence Approaches.

Authors:  Suresh Neethirajan
Journal:  Animals (Basel)       Date:  2022-03-17       Impact factor: 3.231

  3 in total

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