外文摘要：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.
外文关键词：machine learning;prediction;Mastitis;Rumination;Cow;electrical conductivity of milk
作者：Wang, Hao;Xu, Wei;Zhou, Xiaojing;Xu, Chuang;Zhao, Zixuan;Tian, Hong;Deng, Zhaoju
作者单位：China Agr Univ;Katholieke Univ Leuven;Heilongjiang Acad Agr Sci;Heilongjiang Bayi Agr Univ
期刊名称：ANIMALS
期刊影响因子：0.0
出版年份：2024
出版刊次：14（3）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台