Identification of the Estrous Period through Texture Analysis of the Cow Vulva Image

Abstract

The application of artificial insemination is complained by farmers because of frequent failures, which are indicated by failed pregnancies. The failure of pregnant cows due to the incorrect detection of the estrous period by farmers. The purpose of this study was to analyze the texture of the cow vulva image using statistical methods, gray level co-occurrence matrix (GLCM) and gray level run length matrix (GLRLM) methods to identify cattle during estrous periods. So it is hoped that this study will obtain an alternative method of identifying the estrous period of cows more easily, cost effective, quickly and precisely. This study took a sample of 35 images of the vulva of cows, consisting of 20 cows during the estrous period and 15 cows that were not in the estrous period. Furthermore, on the image of the vulva, texture analysis was carried out withusing statistical methods, gray level co-occurrence matrix (GLCM) and gray level run length matrix (GLRLM) methods. The results showed that the classification accuracy with k means cluster texture characteristics statistical method was 77.14%, classification accuracy with k means cluster texture features GLCM method was 54.28% and classification accuracy with k means cluster texture characteristics statistical methods was 71.42 %. So texture analysis of the vulva image can be developed continuously to be a method in identifying the estrous period of cows.