Volume 8 Number 10 (Oct. 2013)
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JCP 2013 Vol.8(10): 2590-2597 ISSN: 1796-203X
doi: 10.4304/jcp.8.10.2590-2597

Estrus Detection in Dairy Cows from Acceleration Data using Self-learning Classification Models

Ling Yin, Tiansheng Hong, and Caixing Liu
College of Informatics, South China Agriculture University, Guangzhou,China

Abstract—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.

Index Terms—accelerometer, k-means, activity index, cow

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Cite: Ling Yin, Tiansheng Hong, and Caixing Liu, " Estrus Detection in Dairy Cows from Acceleration Data using Self-learning Classification Models," Journal of Computers vol. 8, no. 10, pp. 2590-2597, 2013.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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