Cow estrus detection via Discrete Wavelet Transformation and Unsupervised Clustering

Abstract

Estrus is a special periods in the life cycle of female cows. Within this period, they have much more chance to become pregnant. Successfully detecting this period increase the milk and meat productivity of the whole farm. Recently, a potential approach is unsupervised learning on motion data of the cows, similar to human activity recognition based on motion. In particular, an accelerometer is attached to the neck of the cows to measure their acceleration, then the unsupervised algorithm group the measured acceleration time-series. Recent study adopted bag-of-feature and Discrete Fourier Transform for feature extraction, yet it may not reflect the nature of motion data. Thus, we proposed a method based on Discrete Wavelet Transform to get the multi-resolution feature, Dynamic Time Wraping as clustering distance and Iterative-K-Means as clustering algorithm, to better match with the characteristic of cowsâĂŹ movement. The proposed methods demonstrated higher score on human activity recognition dataset with ground truth and more reliable prediction on cow motion dataset.