Automatic recognition of ingestive-related behaviors of dairy cows based on triaxial acceleration
作者机构：College of Electrical and InformationNortheast Agricultural UniversityHarbin 150030China College of Animal Science and TechnologyNortheast Agricultural UniversityHarbin 150030China
出 版 物：《Information Processing in Agriculture》 (农业信息处理（英文）)
年 卷 期：2020年第7卷第3期
页 面：427-443页
核心收录：
学科分类：0905[农学-畜牧学] 081203[工学-计算机应用技术] 08[工学] 09[农学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：This research is financially supported by National Key Research and Development Program of China(2016YFD0700204-02) Research on Intelligent Non-contact Monitoring of Ruminating and Feeding Behavior of Dairy Cows,Heilongjiang Natural Science Foundation(LH2019C025) The“Young Talents”Project of Northeast Agricultural University(17QC19) China Postdoctoral Science Foundation(2017M611346) The Earmarked Fund for China Agriculture Research System(No.CARS-36) The University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province under Grant(UNPYSCT-2018143) The authors are grateful to anonymous reviewers for their comments
主 题：Recognition of ingestive-related behaviors Triaxial acceleration Machine learning
摘 要：Ingestive-related behaviors including feeding and ruminating are important indexes to measure the health and welfare of dairy *** purpose of this study is to develop a method based on triaxial acceleration to automatically recognize feeding and ruminating of dairy *** the experiment,five diary cows raised in a barn were used as experimental subjects.A triaxial acceleration sensor was used as the device to collect jawmovement data of dairy cows,and the behaviors of dairy cows were classified into three categories:feeding,ruminating and other *** features of time-domain and frequency-domain were extracted from the raw acceleration *** machine learning algorithms including k-nearest neighbor,support vector machine and probabilistic neural network were used for the classification and the results based on four different data segment lengths were *** results show that the three algorithms can be used for recognition of feeding and ruminating with high *** the condition that the sampling frequency of the sensor is 5 Hz,the combination of data segment length of 256 and k-nearest neighbor algorithm is the best scheme for recognition of feeding and ruminating in this *** precision and recall of recognition for feeding were 92.8%and 95.6%respectively,and those of recognition for ruminating were 93.7%and 94.3%*** specificity and AUC of recognition for feeding were 96.1%and 0.959 respectively,and those of recognition for ruminating were 97.5%and 0.959 *** will provide an effective method for real-time monitoring of ingestive-related behaviors of dairy cows and lay a foundation for prediction of dairy cows’health status and welfare to further achieve the purpose of disease prediction and adjusting feeding and management methods.
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