Journal of Animal Science and Technology
Korean Society of Animal Science and Technology

Deep learning framework for bovine iris segmentation

Heemoon Yoon1, Mira Park1, Hayoung Lee2, Jisoon Ahn2, Taehyun Lee2, Sang-Hee Lee2,*
1School of Information Communication and Technology, University of Tasmania, Hobart 7005, Australia.
2College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Korea.
*Corresponding Author: Sang-Hee Lee, College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Korea, Republic of. E-mail:

© Copyright 2023 Korean Society of Animal Science and Technology. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: May 10, 2023; Revised: May 22, 2023; Accepted: May 30, 2023

Published Online: Jun 01, 2023


Iris segmentation is an initial step for identifying the biometrics of animals when establishing a traceability system for livestock. In this study, we propose a deep learning framework for pixel-wise segmentation of bovine iris with a minimized use of annotation labels utilizing the BovineAAEyes80 public dataset. The proposed image segmentation framework encompasses data collection, data preparation, data augmentation selection, training of 15 deep neural network (DNN) models with varying encoder backbones and segmentation decoder DNNs, and evaluation of the models using multiple metrics and graphical segmentation results. This framework aims to provide comprehensive and in-depth information on each model's training and testing outcomes to optimize bovine iris segmentation performance. In the experiment, U-Net with a VGG16 backbone was identified as the optimal combination of encoder and decoder models for the dataset, achieving an accuracy and dice coefficient score of 99.50% and 98.35%, respectively. Notably, the selected model accurately segmented even corrupted images without proper annotation data. This study contributes to the advancement of iris segmentation and the establishment of a reliable DNN training framework.

Keywords: Cow; Deep learning; Identification; Iris; Segmentation