CowScreeningDB: A public benchmark dataset for lameness detection in dairy cows

TL;DR

This paper introduces CowScreeningDB, a publicly available multi-sensor dataset collected from dairy cows using an Apple Watch, enabling objective development and comparison of AI-based lameness detection methods.

Contribution

It provides the first transparent, publicly accessible dataset for dairy cow lameness detection and validates a machine learning approach using raw sensor data.

Findings

Successful creation of a multi-sensor dataset from 43 cows

Development of a machine learning classifier for lameness detection

Dataset and method enable objective comparison of future techniques

Abstract

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 dataset which is currently either commercial or privately held. To tackle this limitation, we have introduced CowScreeningDB which was created using sensory data. This dataset was sourced from 43 cows at a dairy located in Gran Canaria, Spain. It consists of a multi-sensor dataset built on data collected using an Apple Watch 6 during the normal daily routine of a dairy cow. Thanks to the collection environment, sampling technique, information regarding the sensors, the applications used for data conversion and storage make the dataset a transparent one. This transparency of data can thus be used for further development of techniques for lameness detection for dairy cows which can be objectively compared. Aside from the public sharing of the dataset, we have also shared a machine-learning technique which classifies the caws in healthy and lame by using the raw sensory data. Hence validating the major objective which is to establish the relationship between sensor data and lameness.