Analysis of Biological Data of Cattle and Wavelet Transform Based Prediction for Optimal Insemination Phase

Abstract

For farmers who maintain dairy cattle, artificial insemination (AI) is one of important events in cattle, because it may lead to lose money by missing out on AI. However, the accuracy for detection depends on the time and number of observations when the estrus behavior and signs for cattle during the estrus season are visually assessed by experts and farmers, and the detection accuracy via experts and farmers is approximately 60%. For farmers, it is obvious that improving reproductive efficiency can save time and money. Therefore, various detection strategies for AI timing such as pedometers and methods based on observation of hormone in estrus have been well studied. Additionally, a detection strategy based on variations for temperature corresponding to ovulation has also been presented. In particular, the accuracy of detection of AI timing based on monitoring the vaginal temperature is greater than that for other methods such as pedometer and so on, i.e., it seems that an optimal timing of AI based on vaginal temperature in cattle is more effective. Although there are some existing results for detection of AI timing based on vaginal temperature and vaginal electrical resistance data, further improvement of accuracy is required in practical use. In this paper, we propose an estimation method for the optimal AI timing by analyzing both vaginal temperature and vaginal electrical resistance data. In our approach, as preprocessing, MaMeMi filter and Gaussian kernel smoother are newly introduced for the purpose of reducing the effect of circadian rhythms and various noises. Moreover, we adopt continuous wavelet transformation to analyze biological data, and NSI (Normalized Spectrum Index) is calculated. Finally, the optimal timing for AI can be estimated by using the Mahalanobis distance. In this paper, we present the proposed estimation algorithm and evaluate the proposed approach.