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CowScreeningDB: A public benchmark database for lameness detection in dairy cows by Shahid Ismail; Moises Diaz; Cristina Carmona-Duarte; Jose Manuel Vilar; Miguel A. Ferrer is a Agricultural and Biological Sciences article available to read on EtoBox.

What is CowScreeningDB: A public benchmark database for lameness detection in dairy cows about?

Lameness is one of the costliest pathological problems affecting dairy animals. It is usually assessed by trained veterinary clinicians who observe features such as gait symmetry or gait parameters as step counts in real time. With the development of artificial intelligence, various modular systems have been proposed to minimize subjectivity in lameness assessment. However, the major limitation in their development is the unavailability of a public database, as most existing ones are either commercial or privately held. To tackle this limitation, we have introduced CowScreeningDB, a multi-sensor database which was built with data from 43 dairy cows. Cows were monitored using smart watches during their normal daily routine. The uniqueness of the database lies in its data collection environment, sampling methodology, detailed sensor information, and the applications used for data conversion and storage, which ensure transparency and replicability. This data transparency makes Cow-ScreeningDB a valuable and objectively comparable resource for further development of techniques for lameness detection for dairy cows. In addition to publicly sharing the database, we present a machine lear

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Author

Shahid Ismail; Moises Diaz; Cristina Carmona-Duarte; Jose Manuel Vilar; Miguel A. Ferrer

Publisher

Elsevier BV

Published

2024

Language

EN

Field

Agricultural and Biological Sciences (Physical Sciences)