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

Abstract

IntroductionLameness 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).MethodsThis 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 (κ) 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.ResultsIn 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.DiscussionThis 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.

Human-animal interactions and machine-animal interactions in animals under human care: A summary of stakeholder and researcher perceptions and future directions

“…For example, concerns around cost could be overcome by undertaking research to improve the effectiveness and efficiency of technologies. Efficiency could be improved by using machine learning to process large amounts of data to provide highly detailed information about the behaviours and states of monitored animals, in some cases using ambient sensors rather than instrumenting individual animals (Hansen et al 2018 ; Anagnostopoulos et al 2023 ; Siachos et al 2023 ). This means that once the technology is in place, monitoring an increasing number of animals does not necessarily increase costs exponentially.…”