Investigation of Individual Identification Methods in The Cow Herd

Abstract

This study examines methods for individual identification of Holsteins raised in freestall format for the purpose of efficient individual management of dairy cows, in response to the aging of dairy farmers in Japan and the increase in the number of dairy cows being raised. In particular, we aim to develop a machine learning model that generates individual names and bounding boxes in real time from camera images. In order to identify individual Holsteins using their patterns, we will build a model to estimate their orientation using images taken from eight different directions of the Holstein and real-(breakpoint)time video. In the experiment, object detection using YOLOv8, keypoint detection using AnimalPose, and instance segmentation will be used to develop a machine learning model to estimate orientation based on these data. The results show that even overlapping cows can be detected and identified with high accuracy.