文章基本信息

标题：Estrus Detection in Dairy Cows from Acceleration Data using Self-learning Classification Models
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作者：Yin, Ling ; Hong, Tiansheng ; Liu, Caixing 等
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2013
卷号：8
期号：10
页码：2590-2597
DOI：10.4304/jcp.8.10.2590-2597
语种：English
出版社：Academy Publisher
摘要：Automatic estrus detection techniques in dairy cows have been present by different traits. Pedometers and accelerators are the most common sensor equipment. Most of the detection methods are associated with the supervised classification technique, which the training set becomes a crucial reference. The training set obtained by visual observation is subjective and time consuming. Another limitation of this approach is that it usually does not consider the factors affecting successful alerts, such as the discriminative figure, activity type of cows, the location and direction of the sensor node placed on the neck collar of a cow. This paper presents a novel estrus detection method that uses k-means clustering algorithm to create the training set online for each cow. And the training set is finally used to build an activity classification model by SVM. The activity index counted by the classification results in each sampling period can measure cow’s activity variation for assessing the onset of estrus. The experimental results indicate that the peak of estrus time are higher than that of non-estrus time at least twice in the activity index curve, and it can enhance the sensitivity and significantly reduce the error rate.
关键词：accelerometer;k-means;activity index;cow