An evaluation of early intervention protocols based on camera-based autonomous mobility score trends

Abstract

Lameness is a major animal welfare issue in the dairy indus­try and its measurement is a component of both industry- and processor-led animal welfare audits. Although lameness is highly prevalent on dairy farms, farm personnel have difficul­ties identifying mild cases of lameness. Reasons for this diffi­culty include the time, human resources and training required to detect lameness, along with the lack of belief that early inter­vention works. Automatizing the process of lameness detection through the implementation of autonomous camera-based deep learning will provide dairy farms with an effective and easy-to-implement tool to reduce lameness duration. Combining early and accurate lameness detection with appropriate treatment protocols will result in lower levels of lameness. The objective of this project was to determine if changes in camera-based au­tonomous mobility scores could be used to reduce the duration of lameness.