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Research on a Dynamic Algorithm for Cow Weighing Based on an SVM and Empirical Wavelet Transform

机译：基于SVM和经验小波变换的母牛称重动态算法研究

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摘要

Weight is an important indicator of the growth and development of dairy cows. The traditional static weighing methods require considerable human and financial resources, and the existing dynamic weighing algorithms do not consider the influence of the cow motion state on the weight curve. In this paper, a dynamic weighing algorithm for cows based on a support vector machine (SVM) and empirical wavelet transform (EWT) is proposed for classification and analysis. First, the dynamic weight curve is obtained by using a weighing device placed along a cow travel corridor. Next, the data are preprocessed through valid signal acquisition, feature extraction, and normalization, and the results are divided into three active degrees during motion for low, medium, and high grade using the SVM algorithm. Finally, a mean filtering algorithm, the EWT algorithm, and a combined periodic continuation-EWT algorithm are used to obtain the dynamic weight values. Weight data were collected for 910 cows, and the experimental results displayed a classification accuracy of 98.6928%. The three algorithms were used to calculate the dynamic weight values for comparison with real values, and the average error rates were 0.1838%, 0.6724%, and 0.9462%. This method can be widely used at farms and expand the current knowledgebase regarding the dynamic weighing of cows.
机译：重量是奶牛生长和发展的重要指标。传统的静态称重方法需要相当大的人力和财力资源，现有的动态称重算法不考虑牛运动状态对重量曲线的影响。本文提出了一种基于支持向量机（SVM）和经验小波变换（EWT）的母牛动态称重算法，用于分类和分析。首先，通过使用沿着牛行程走廊放置的称重装置获得动态权重曲线。接下来，通过有效的信号采集，特征提取和归一化进行预处理数据，并且在使用SVM算法的低，介质和高级的运动期间将结果分为三个有效度。最后，使用平均滤波算法，EWT算法和组合的周期性连续-EWT算法来获得动态权重值。收集了910奶牛的重量数据，实验结果显示了98.6928％的分类准确性。三种算法用于计算与实际值相比的动态重量值，平均误差率为0.1838％，0.6724％和0.9462％。该方法可广泛应用于农场，并扩大关于奶牛的动态称量的当前知识库。

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