Automatic Deep Prediction: Computational early detection of dairy cattle Mastitis using semi-supervised learning : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University 

Abstract

Dairy cattle Mastitis is considered as one of the most notable and costly diseases in dairy industry worldwide; this disease severely affects dairy cattle and results in a costly cattle treatment, and causes a huge decrease in milk produced from sick cows. Mastitis also may cause other diseases that may affect the herd due to the bacterial infection. The total Mastitis cost to dairy industry in New Zealand is up near $280 million a year; this cost includes drop in milk production, low-grade milk quality, cattle treatment cost and other costs. This research includes the examination and analysis of data collected from a commercial robotic dairy farm, in order to design and build computational models that can help early and accurate detection of Mastitis in dairy cattle herds. Unlike previous studies, this research approaches the early detection by studying pre-clinical Mastitis. Pre-clinical Mastitis includes sick instances in any form prior to clinical Mastitis, including the form of subclinical Mastitis, which is the earliest form of the disease. This approach of defining early detection complies with the understanding of cattle Mastitis as a continuum of different forms/stages, which is more realistic than thinking of subclinical Mastitis as an isolated status of the disease. Early and accurate Mastitis detection helps cut treatment costs, control the disease, retain milk production levels and maintain milk quality grade. In addition to cutting financial costs, early detection helps cows by protecting them and relieving pain caused by the disease. Computational models can help achieve these by helping farmers to adopt timely and suitable cattle treatment regime, and by preventing healthy cows from being infected. The results of this research allow viewing dairy cattle Mastitis detection from a different angle, which brings about a broader understanding of some of the early signs and symptoms, leading to better control and management of the disease.