Deep Moo : an artificial intelligence approach to lameness detection in dairy cows

Abstract

This thesis provides a comprehensive investigation into the potential of artificial intelligence (AI) technologies, specifically deep learning and computer vision, for the enhancement of dairy cow health management and identification. The research introduces innovative methodologies for the detection of lameness in dairy cows and for cow identification through matching analysis of nose patterns. A significant accomplishment of this study is the development of an autonomous lameness detection system. Utilising a modified Mask-RCNN, the SORT algorithm, and the CatBoost gradient boosting algorithm on standard camera data, this system demonstrates significant potential with a lameness detection accuracy of 98%. Importantly, this accuracy is benchmarked against experts in visual lameness assessments, which are generally considered the gold standard in lameness detection. The system also achieves a lameness severity classification accuracy of 94%. Concurrently, the study explores the possibility of using facial expressions, with a focus on ear movements, as an alternative lameness indicator. Despite the lesser accuracy of 60%, benchmarked against the same veterinary assessments, this investigation highlights the potential significance of cow facial expressions in lameness detection. It introduces a user-friendly system developed on micro-service architecture for this purpose. The thesis also presents a novel and non-invasive technique for cow identification using nose patterns, achieving an accuracy rate of 76.6%. This approach suggests promising appli- x cations for the development of cost-effective, non-invasive, and scalable animal identification systems across species with similar nose structures. While the research findings underscore the profound potential of AI in dairy farming, it is acknowledged that further refinement and validation in more diverse and larger farm settings are needed to improve the accuracy of facial expression-based lameness detection and nose pattern-based identification. The potential integration of these detection systems with automated alerting mechanisms and cow management systems, and the exploration of other AI-detectable health indicators in cows, may offer further improvements in dairy farm management. In a wider context, this research signifies a pivotal advancement in applying AI technologies to improve animal welfare and streamline farm management. It suggests transformative possibilities for not just the dairy industry, but the entire livestock farming sector, enhancing care, productivity, and sustainability. As these advanced AI systems continue to be refined and expanded, they have the potential to redefine our approaches to monitoring and caring for various animal species.