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Turkish Journal of Veterinary & Animal Sciences

Author ORCID Identifier

ALI SAIFUDIN: 0009-0008-2028-8116

SRI PANTJA MADYAWATI: 0000-0002-5864-439X

IMAM YUADI: 0000-0002-7848-1671

RIMAYANTI RIMAYANTI: 0000-0002-6949-522X

IMAM MUSTOFA: 0000-0003-4543-1659

RIRIES RULANINGTYAS: 0000-0001-7058-1566

TEDDY SURYA GUNAWAN: 0000-0003-3345-4669

ADNAN RAHMAT ANOM BESARI: 0000-0001-5916-5019

DOI

10.55730/1300-0128.4355

Abstract

Cow behavior is a crucial indicator for monitoring health, reproductive status, and welfare in livestock management. However, methods that rely on wearable devices often face significant challenges, including high costs, maintenance difficulties, and potential impacts on animal welfare. To address these limitations, this study explored the potential of using YOLOv8, a cutting-edge computer vision model, for non-invasive monitoring of cow behavior. The research methodology involved four key steps: data collection, preliminary data processing, model training, and validation. The findings revealed that YOLOv8 is capable of accurately detecting and localizing key cow behaviors—lying, standing, eating, and ruminating—achieving a mean average precision (mAP) of 0.778 at a 0.5 intersection over union (IoU) threshold. Despite the promising results, the model’s performance is notably affected by occlusion, which remains a primary challenge. Nevertheless, the outcomes indicate that YOLOv8 is a viable tool for recognizing cow behavior, offering a significant step forward in precision livestock farming and addressing the growing need for efficient and welfare-oriented livestock management practices.

Keywords

Cow behavior, lying, standing, eating, ruminating, YOLOv8

First Page

190

Last Page

197

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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