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

Date published

2024-04-18

Free to read from

2024-05-13

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Journal ISSN

Volume Title

Publisher

IEEE

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Type

Article

ISSN

1551-3203

Format

Citation

Khan 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, Volume 20, Issue 7, July 2024, pp. 9508-9517

Abstract

Mastitis, 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%.

Description

Software Description

Software Language

Github

Keywords

Animal health informatics, automated detection, clinical mastitis, decision support system, IoT wearable sensor

DOI

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Attribution 4.0 International

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Funder/s

U.K. Research and Innovation (Grant Number: 104989)