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2026 Vol.6, Issue 2 Preview Page

Research Article

Hanwoo face-based individual identification using deep learning

딥러닝을 이용한 한우 안면 기반 개체 인식

Tae-eui An1
,
Soo-Hyun Cho2
,
Dae-Hyun Lee34*
안 태의1
,
조 수현2
,
이 대현34*
1Undergraduate Student, Department of Smart Agricultural System Mechanical Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea
2Graduate Student, Department of Agricultural Mechanical Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea
3Professor, Department of Smart Agricultural System Mechanical Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea
4Professor, Department of Agricultural Mechanical Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea
1충남대학교 스마트농업시스템기계공학과 학부생
2충남대학교 농업기계공학과 대학원생
3충남대학교 스마트농업시스템기계공학과 교수
4충남대학교 농업기계공학과 교수
*Corresponding Author
30 June 2026. pp. 61-74
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Abstract

Individual identification in livestock farms is a fundamental technology for precision livestock management, history tracking, and productivity management. Conventional identification methods, such as ear tags, sensors, and RFID devices, require physical attachment to animals and have limitations related to device loss, damage, and management burden. To overcome these limitations, camera-based non-contact identification technologies have received increasing attention. However, in practical livestock environments, existing cattle are repeatedly shipped out and new cattle are continuously introduced, requiring a technology that can reflect newly added individuals in the identification system. Therefore, in this study, we proposed a deep learning-based individual registration and identification framework using Hanwoo facial images to register new cattle with a small number of facial images. The proposed framework learns facial features from existing cattle, generates representative features from registration images of new cattle, and identifies individuals by comparing the similarity between input image features and registered features. In addition, the effects of the number and viewpoint composition of registration images on new-cattle identification performance were analyzed. Results showed a test accuracy of 98.68% for the initial identification model, and the highest overall average accuracy and new-cattle accuracy were 94.8% and 91.0%, respectively. Although identification performance varied depending on registration conditions, the proposed framework confirmed that newly introduced cattle can be reflected in the identification system using only a small number of registration images. Based on the results, the proposed approach is expected to improve the field applicability and operational efficiency of non-contact Hanwoo identification systems.

Keywords
Precision livestock farming
Hanwoo
Face identification
Cattle registration
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Information
  • Publisher :The Korean Society for Agricultural Machinery
  • Publisher(Ko) :한국농업기계학회
  • Journal Title :Journal of Agricultural Machinery Engineering
  • Journal Title(Ko) :농업기계공학
  • Volume : 6
  • No :2
  • Pages :61-74
  • Received Date : 2026-06-16
  • Revised Date : 2026-06-24
  • Accepted Date : 2026-06-24
  • DOI :https://doi.org/10.12972/jame.2026.6.2.2
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