Using milk flow profiles for subclinical mastitis detection

Abstract

Mastitis is a significant disease on dairy farms and can have serious negative animal performance and economic consequences if not controlled. While clinical mastitis is often easily identified due to visibly abnormal milk, subclinical mastitis presents a more insidious challenge. Somatic cell count (SCC) is commonly used to monitor and detect subclinical mastitis, however, SCC is not available at a high sampling frequency rate at the cow level on most farms due to the manual effort involved in collecting it. With the rise of precision dairy farming technologies such as milk meters, however, there is increasing interest in using data-driven approaches (especially approaches using machine learning) for detecting subclinical mastitis based on indicators more easily collected by modern sensors. In this article we introduce milk flow profiles, a new, easy-to-collect data type that can replace more difficult-to-collect data sources (e.g., those that require laboratory tests or manual measurements) in precision dairy farming. The results of our experiments demonstrate that milk flow profiles, combined with other easily accessible milking machine data, can be employed to train machine learning models that accurately detect subclinical mastitis (as evidenced by high SCC measurements), with an AUC of 0.793. Moreover, these models perform better than models trained using features from milk characteristic data that are expensive to collect and are only collected at low frequency on commercial farms. Our experiments used data from 16 weeks of milking events from 285 cows on Irish farms, and their results demonstrate the value of milk flow profiles as an easily accessible and valuable data source for precision dairy farming applications.