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The Prediction of Clinical Mastitis in Dairy Cows Based on Milk Yield, Rumination Time, and Milk Electrical Conductivity Using Machine Learning Algorithms

文献类型： 外文期刊

作者： Tian, Hong 1 ; Zhou, Xiaojing 1 ; Wang, Hao 3 ; Xu, Chuang 4 ; Zhao, Zixuan 2 ; Xu, Wei 5 ; Deng, Zhaoju 4 ;

作者机构： 1.Heilongjiang Bayi Agr Univ, Coll Sci, 5 Xinyang Rd, Daqing 163319, Peoples R China

2.Heilongjiang Bayi Agr Univ, Coll Anim Sci & Vet Med, Heilongjiang Prov Key Lab Prevent & Control Bovine, 5 Xinyang Rd, Daqing 163319, Peoples R China

3.Heilongjiang Acad Agr Sci, Anim Husb & Vet Branch, Qiqihar 161005, Peoples R China

4.China Agr Univ, Coll Vet Med, 17 Tsinghua East Rd, Beijing 100107, Peoples R China

5.Katholieke Univ Leuven, Dept Biosyst, Div Anim & Human Hlth Engn, Oude Markt 13, B-3000 Leuven, Belgium

关键词： cow; mastitis; prediction; rumination; electrical conductivity of milk; machine learning

期刊名称：ANIMALS （ 影响因子：3.0； 五年影响因子：3.2 ）

ISSN： 2076-2615

年卷期： 2024 年 14 卷 3 期

页码：

收录情况： SCI

摘要： In commercial dairy farms, mastitis is associated with increased antimicrobial use and associated resistance, which may affect milk production. This study aimed to develop sensor-based prediction models for naturally occurring clinical bovine mastitis using nine machine learning algorithms with data from 447 mastitic and 2146 healthy cows obtained from five commercial farms in Northeast China. The variables were related to daily activity, rumination time, and daily milk yield of cows, as well as milk electrical conductivity. Both Z-standardized and non-standardized datasets pertaining to four specific stages of lactation were used to train and test prediction models. For all four subgroups, the Z-standardized dataset yielded better results than those of the non-standardized one, with the multilayer artificial neural net algorithm showing the best performance. Variables of importance had a similar rank in this algorithm, indicating the consistency of these variables as predictors for bovine mastitis in commercial farms with similar automatic systems. Moreover, the peak milk yield (PMY) of mastitic cows was significantly higher than that of healthy cows (p < 0.005), indicating that high-yielding cattle are more prone to mastitis. Our results show that machine learning algorithms are effective tools for predicting mastitis in dairy cows for immediate intervention and management in commercial farms.

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