Dairy Cattle's Behavior Recognition Method Based on Support Vector Machine Classification Model

被引：0

作者：

Ren X. ^{[1
]}

Liu G. ^{[1
,2
]}

Zhang M. ^{[1
,2
]}

Si Y. ^{[3
]}

Zhang X. ^{[1
]}

Ma L. ^{[1
,3
]}

机构：

[1] Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing

[2] Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing

[3] College of Information Science and Technology, Hebei Agricultural University, Baoding

来源：

Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2019年 / 50卷

关键词：

Accelerometer; Behavior classification; Cows; Ruminating; Support vector machine;

D O I：

10.6041/j.issn.1000-1298.2019.S0.045

中图分类号：

学科分类号：

摘要：

Aiming at the problems of manpower behavior spend and low monitoring accuracy of dairy cows, a cow behavior classification method was proposed based on that firefly algorithm to optimize support vector machine parameters by taking advantage of the data which obtained by wireless transmission neck ring. The method optimized the parameters of the support vector machine by using the firefly optimization algorithm to achieve the optimal classification accuracy. The experimental results showed that the wireless transmission collars can collect and transmit the cow neck activity information simultaneously. And the algorithm could effectively distinguish the three behaviors of different cows' feeding, ruminating and drinking. The applicability was greatly improved. Among them, the optimal precision, sensitivity and accuracy rate were 97.28%, 97.03% and 98.02%, respectively. Compared with the conventional support vector machine algorithm, using the method proposed, the classification accuracy, sensitivity and accuracy of the same cow were increased by 13.39, 28.2 and 18.8 percentage points, respectively; the classification accuracy, sensitivity and accuracy of different dairy cows were increased by 0.74, 2.24 and 2.12 percentage points, respectively. The research results can provide technical support for further research on abnormal behavior detection and intelligent early warning of diseases in dairy cows. © 2019, Chinese Society of Agricultural Machinery. All right reserved.

引用

页码：290 / 296

页数：6

共 24 条

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