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

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.

The Hazardous Effect of Short Birth Interval on Physical Growth. A 4 - Case Study

A case study of the hazardous effect of short birth interval upon the physical growth of four siblings of a middle-class, well-educated family has been undertaken. For the purpose of the study the records of four siblings were retrospectively evaluated to the age of 18 months. This study revealed that short interval between birth and the next conception exerted great influence on the physical growth as shown by the weight curve.