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Automatic individual identification of Holstein dairy cows using tailhead images

文献类型： 外文期刊

作者： Li, Wenyong 1 ; Ji, Zengtao 1 ; Wang, Lin 3 ; Sun, Chuanheng 1 ; Yang, Xinting 1 ;

作者机构： 1.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Minist Agr, Key Lab Informat Technol Agr, Beijing 100097, Peoples R China

3.Tianjin Univ Sci & Technol, 1038 Dagu Nanlu, Tianjin, Peoples R China

关键词： Dairy cow; Image identification; Computer vision; Zernike moments; Precision livestock

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2017 年 142 卷

页码：

收录情况： SCI

摘要： The implementation of dairy cow identification will be of great significance in precision animal management based on computer vision. In this study, a computer vision technique to identify the individual dairy cows automatically was proposed and evaluated. The tailhead image, which was used as a Region of Interest (ROI), was captured in a dairy farm. Zernike moments were used as descriptors of shape characteristics for the white pattern on the ROI. Two groups of Zernike moments were extracted from the preprocessed image and classified using four alternative classifiers, namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), artificial neural network (ANN) and support vector machines (SVM). The QDA classifier had the highest value, 99.7%, while the SVM classifier had the highest precision, 99.6%. Comprehensively, the QDA and SVM classifiers presented the best performance, with equal F-1 score of 0.995. These results show that the low-order Zernike moment feature, along with the QDA and SVM algorithms is an effective approach for individual dairy cow identification and has significant applications in precision animal management.

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