Initial validation of an intelligent video surveillance system for automatic detection of dairy cattle lameness

Anagnostopoulos, Alkiviadis ORCID: 0000-0002-5193-858X, Griffiths, Bethany E ORCID: 0000-0002-2698-9561, Siachos, Nektarios ORCID: 0000-0001-7670-4950, Neary, Joseph ORCID: 0000-0001-8438-2234, Smith, Robert F ORCID: 0000-0003-0944-310X and Oikonomou, Georgios ORCID: 0000-0002-4451-4199
(2023) Initial validation of an intelligent video surveillance system for automatic detection of dairy cattle lameness. FRONTIERS IN VETERINARY SCIENCE, 10. 1111057-.

[thumbnail of Anagnostopoulos CattleEye fvets-10-1111057.pdf] PDF
Anagnostopoulos CattleEye fvets-10-1111057.pdf - Open Access published version

Download (479kB) | Preview


<h4>Introduction</h4>Lameness is a major welfare challenge facing the dairy industry worldwide. Monitoring herd lameness prevalence, and early detection and therapeutic intervention are important aspects of lameness control in dairy herds. The objective of this study was to evaluate the performance of a commercially available video surveillance system for automatic detection of dairy cattle lameness (CattleEye Ltd).<h4>Methods</h4>This was achieved by first measuring mobility score agreement between CattleEye and two veterinarians (Assessor 1 and Assessor 2), and second, by investigating the ability of the CattleEye system to detect cows with potentially painful foot lesions. We analysed 6,040 mobility scores collected from three dairy farms. Inter-rate agreement was estimated by calculating percentage agreement (PA), Cohen's kappa (<i>κ</i>) and Gwet's agreement coefficient (AC). Data regarding the presence of foot lesions were also available for a subset of this dataset. The ability of the system to predict the presence of potentially painful foot lesions was tested against that of Assessor 1 by calculating measures of accuracy, using lesion records during the foot trimming sessions as reference.<h4>Results</h4>In general, inter-rater agreement between CattleEye and either human assessor was strong and similar to that between the human assessors, with PA and AC being consistently above 80% and 0.80, respectively. Kappa agreement between CattleEye and the human scorers was in line with previous studies (investigating agreement between human assessors) and within the fair to moderate agreement range. The system was more sensitive than Assessor 1 in identifying cows with potentially painful lesions, with 0.52 sensitivity and 0.81 specificity compared to the Assessor's 0.29 and 0.89 respectively.<h4>Discussion</h4>This pilot study showed that the CattleEye system achieved scores comparable to that of two experienced veterinarians and was more sensitive than a trained veterinarian in detecting painful foot lesions.

Item Type: Article
Uncontrolled Keywords: cattle lameness, automated system, foot lesions, mobility scoring, artificial intelligence
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Infection, Veterinary and Ecological Sciences
Depositing User: Symplectic Admin
Date Deposited: 13 Jun 2023 14:59
Last Modified: 07 Jul 2023 02:51
DOI: 10.3389/fvets.2023.1111057
Related URLs: