Delineating mastitis cases in dairy cows: development of an IoT-enabled intelligent decision support system for dairy farms

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dc.contributor.authorKhan, Mohammad Farhan
dc.contributor.authorThorup, Vivi Mørkøre
dc.contributor.authorLuo, Zhenhua
dc.date.accessioned2024-05-13T13:02:42Z
dc.date.available2024-05-13T13:02:42Z
dc.date.issued2024-04-18
dc.description.abstractMastitis, an intramammary bacterial infection, is not only known to adversely affect the health of a dairy cow but also to cause significant economic loss to the dairy industry. The severity and spread of mastitis can be restrained by identifying the early signs of infection in the cows through an intelligent decision support system. Early intervention and control of infection largely depend on the availability of on-site high throughput machinery, which can analyze milk samples regularly. However, due to limited resources, marginal and small farms usually cannot afford such high-end machinery, hence, the financial loss in such farms due to mastitis may become significant. To overcome such limitations, this article proposes a low-complexity yet affordable automated system for accurate prediction of early signs of clinical mastitis infection in dairy cows. In this work, behavioral data collected through Internet of Things (IoT)-enabled wearable sensors for cows is utilized to develop a support vector machine (SVM) model for the daily prediction of mastitis cases in a dairy farm. The dataset from the research herd utilizes the information of 415 cows collected in the span of 4.75 years in which 75 cows had mastitis. In addition to relevant behavioral features, other statistically significant features, such as daily milk yield, lactation period, and age are also utilized as features. Our study indicates that the SVM model comprising a subset of behavioral and nonbehavioral features can deliver a mastitis prediction accuracy of 89.2%.en_UK
dc.description.sponsorshipU.K. Research and Innovation (Grant Number: 104989)en_UK
dc.identifier.citationKhan MF, Thorup VM, Luo Z. (2024) Delineating mastitis cases in dairy cows: development of an IoT-enabled intelligent decision support system for dairy farms. IEEE Transactions on Industrial Informatics. Available online 18 April 2024en_UK
dc.identifier.eissn1941-0050
dc.identifier.issn1551-3203
dc.identifier.urihttps://doi.org/10.1109/TII.2024.3384594
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21597
dc.language.isoen_UKen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAnimal health informaticsen_UK
dc.subjectautomated detectionen_UK
dc.subjectclinical mastitisen_UK
dc.subjectdecision support systemen_UK
dc.subjectIoT wearable sensoren_UK
dc.titleDelineating mastitis cases in dairy cows: development of an IoT-enabled intelligent decision support system for dairy farmsen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2024-03-23

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