Can We Detect Mastitis earlier than Farmers?

TL;DR

This study develops machine learning models to detect mastitis infections earlier than farmers can, focusing on subclinical and clinical cases using different feature sets and modeling frameworks.

Contribution

Introduces two novel modeling frameworks, SMA and AMA, for early detection of mastitis, incorporating new feature sets based on cow and farm characteristics.

Findings

SMA outperforms AMA in detecting subclinical mastitis

Feature sets based on cow and farm means improve detection accuracy

AMA can detect clinical mastitis at any milking, offering broader detection

Abstract

The aim of this study was to build a modelling framework that would allow us to be able to detect mastitis infections before they would normally be found by farmers through the introduction of machine learning techniques. In the making of this we created two different modelling framework's, one that works on the premise of detecting Sub Clinical mastitis infections at one Somatic Cell Count recording in advance called SMA and the other tries to detect both Sub Clinical mastitis infections aswell as Clinical mastitis infections at any time the cow is milked called AMA. We also introduce the idea of two different feature sets for our study, these represent different characteristics that should be taken into account when detecting infections, these were the idea of a cow differing to a farm mean and also trends in the lactation. We reported that the results for SMA are better than those created by AMA for Sub Clinical infections yet it has the significant disadvantage of only being able to classify Sub Clinical infections due to how we recorded Sub Clinical infections as being any time a Somatic Cell Count measurement went above a certain threshold where as CM could appear at any stage of lactation. Thus in some cases the lower accuracy values for AMA might in fact be more beneficial to farmers.