Show simple item record

dc.contributor.authorKhamaysa Hajaya, Moaathen
dc.contributor.authorSamarasinghe, Sandhyaen
dc.contributor.authorKulasiri, Gamalathge D.en
dc.contributor.authorLopez Benavides, M.en
dc.contributor.editorElsawah, S.en
dc.date.accessioned2020-01-30T02:57:36Z
dc.date.available2019-12-01en
dc.date.issued2019-12en
dc.identifier.isbn9780975840092en
dc.identifier.urihttps://hdl.handle.net/10182/11371
dc.description.abstractDairy cattle Mastitis is one of the most notable and costly diseases in dairy industry worldwide. The total Mastitis cost to dairy industry in New Zealand is up near $280 million a year; this cost includes drop in milk production, cattle treatment 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 a computational model that can help efficient and accurate detection of Mastitis in dairy cattle herds. 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, efficient 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. For this study, robotic data have been collected for 12 months from a barn-based dairy farm at Makikihi in South Canterbury - New Zealand. At data collection time, that farm was the largest dairy farm in the world in terms of the number of milking-robots under one roof (24 milking robots in one barn). The collected dataset contains sensor data of more than 1,900 cows being milked more than 1.1 million times during the time of data collection. Having about 29,000 milking instances fully labelled (healthy/sick), a deep neural network (DNN) was used to build, train and validate a classification model using variable combinations, including variables that have not been studied before. The model has shown the ability to perform detection tasks with a high level of accuracy; with Specificity (Sp) of 99%, and Sensitivity (Se) of 97%. With this high and stable Sp, and the relatively high Se, the proposed model avoids the problem of false positive alerts. By using deep neural networks to build a Mastitis detection model, this study exploits the main characteristic that gives deep learning predominance compared with other techniques - representation learning, which means that the trained models can extract patterns that used to be ignored by other techniques, to present a robust definition of Mastitis, using real-world sensor data, generated by milking robots in a commercial dairy farm, including data for previously unexploited features. The results of this study allow viewing dairy cattle Mastitis detection from a different angle, which brings about a broader understanding of some of the signs and symptoms of Mastitis, leading to better control and management of the disease.en
dc.format.extent35-41en
dc.language.isoenen
dc.publisherModelling and Simulation Society of Australia and New Zealanden
dc.relationThe original publication is available from - Modelling and Simulation Society of Australia and New Zealand - https://doi.org/10.36334/modsim.2019.a1.khamaysahajaya - https://mssanz.org.au/modsim2019/en
dc.relation.urihttps://doi.org/10.36334/modsim.2019.a1.khamaysahajayaen
dc.rights© The Authors and Modelling and Simulation Society of Australia and New Zealand Inc.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.source23rd International Congress on Modelling and Simulation (MODSIM2019)en
dc.subjectmastitisen
dc.subjectdeep neural networksen
dc.subjectdairyen
dc.subjectkerasen
dc.titleDetection of dairy cattle Mastitis: Modelling of milking features using deep neural networksen
dc.typeConference Contribution - Published
lu.contributor.unitLincoln Universityen
lu.contributor.unitFaculty of Agriculture and Life Sciencesen
lu.contributor.unitDepartment of Wine, Food and Molecular Biosciencesen
lu.contributor.unitFaculty of Environment, Society and Designen
lu.contributor.unitDepartment of Environmental Managementen
dc.identifier.doi10.36334/modsim.2019.a1.khamaysahajayaen
dc.subject.anzsrc070203 Animal Managementen
dc.subject.anzsrc070703 Veterinary Diagnosis and Diagnosticsen
dc.subject.anzsrc080108 Neural, Evolutionary and Fuzzy Computationen
dc.subject.anzsrc080110 Simulation and Modellingen
dc.relation.isPartOfMODSIM2019, 23rd International Congress on Modelling and Simulationen
pubs.finish-date2019-12-06en
pubs.notesTheme: Supporting evidence-based decision making: the role of modelling and simulation Publicly available for downloaden
pubs.organisational-group/LU
pubs.organisational-group/LU/Agriculture and Life Sciences
pubs.organisational-group/LU/Agriculture and Life Sciences/WFMB
pubs.organisational-group/LU/Faculty of Environment, Society and Design
pubs.organisational-group/LU/Faculty of Environment, Society and Design/DEM
pubs.organisational-group/LU/Research Management Office
pubs.organisational-group/LU/Research Management Office/QE18
pubs.publication-statusPublished onlineen
pubs.publisher-urlhttps://mssanz.org.au/modsim2019/en
pubs.start-date2019-12-01en
dc.rights.licenceAttributionen
lu.identifier.orcid0000-0001-8744-1578
lu.identifier.orcid0000-0003-2943-4331
lu.subtypeConference Paperen


Files in this item

Default Thumbnail
Default Thumbnail

This item appears in the following Collection(s)

Show simple item record

Creative Commons Attribution
Except where otherwise noted, this item's license is described as Creative Commons Attribution