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Cow behavior recognition based on image analysis and activities

文献类型： 外文期刊

作者： Gu Jingqiu 1 ; Wang Zhihai 1 ; Gao Ronghua 2 ; Wu Huarui 2 ;

作者机构： 1.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Minist Agr, Key Lab Agri Informat, Beijing 100097, Peoples R China

4.Beijing Engn Res Ctr Agr Internet Things, Beijing 100097, Peoples R China

关键词： cow behavior;target segmentation;image entropy;image moment;activities;intelligent analysis

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：2.032； 五年影响因子：2.137 ）

ISSN： 1934-6344

年卷期： 2017 年 10 卷 3 期

页码：

收录情况： SCI

摘要： For the rapid and accurate identification of cow reproduction and healthy behavior from mass surveillance video, in this study, 400 head of young cows and lactating cows were taken as the research object and analyzed cow behavior from the dairy activity area and milk hall ramp. The method of object recognition based on image entropy was proposed, aiming at the identification of motional cow object behavior against a complex background. Calculating a minimum bounding box and contour mapping were used for the real-time capture of rutting span behavior and hoof or back characteristics. Then, by combining the continuous image characteristics and movement of cows for 7 d, the method could quickly distinguish abnormal behavior of dairy cows from healthy reproduction, improving the accuracy of the identification of characteristics of dairy cows. Cow behavior recognition based on image analysis and activities was proposed to capture abnormal behavior that has harmful effects on healthy reproduction and to improve the accuracy of cow behavior identification. The experimental results showed that, through target detection, classification and recognition, the recognition rates of hoof disease and heat in the reproduction and health of dairy cows were greater than 80%, and the false negative rates of oestrus and hoof disease were 3.28% and 5.32%, respectively. This method can enhance the real-time monitoring of cows, save time and improve the management efficiency of large-scale farming.

  • 相关文献

[1]Cow Behavioral Recognition Using Dynamic Analysis. Gao Ronghua,Gu JingQiu,Liang Jubao,Gao Ronghua,Gu JingQiu,Liang Jubao,Gao Ronghua,Gu JingQiu,Liang Jubao,Gao Ronghua,Gu JingQiu,Liang Jubao. 2017

[2]Agricultural image target segmentation based on fuzzy set. Gao, Ronghua,Wu, Huarui,Gao, Ronghua,Wu, Huarui,Gao, Ronghua,Wu, Huarui. 2015

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