Digital Twin-Driven Teat Localization and Shape Identification for Dairy Cow (Student Abstract)

Aarushi Gupta, Yuexing Hao, Yuting Yang, Tiancheng Yuan, Matthias Wieland, Parminder S. Basran, Ken Birman

AAAI 2024 pp. 23510-23511

doi:10.1609/AAAI.V38I21.30450 /aaai/2024/gupta2024aaai-digital/

Abstract

Dairy owners invest heavily to keep their animals healthy. There is good reason to hope that technologies such as computer vision and artificial intelligence (AI) could reduce costs, yet obstacles arise when adapting these advanced tools to farming environments. In this work, we applied AI tools to dairy cow teat localization and teat shape classification, obtaining a model that achieves a mean average precision of 0.783. This digital twin-driven approach is intended as a first step towards automating and accelerating the detection and treatment of hyperkeratosis, mastitis, and other medical conditions that significantly burden the dairy industry.

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Cite

Text

Gupta et al. "Digital Twin-Driven Teat Localization and Shape Identification for Dairy Cow (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30450

Markdown

[Gupta et al. "Digital Twin-Driven Teat Localization and Shape Identification for Dairy Cow (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/gupta2024aaai-digital/) doi:10.1609/AAAI.V38I21.30450

BibTeX

@inproceedings{gupta2024aaai-digital,
  title     = {{Digital Twin-Driven Teat Localization and Shape Identification for Dairy Cow (Student Abstract)}},
  author    = {Gupta, Aarushi and Hao, Yuexing and Yang, Yuting and Yuan, Tiancheng and Wieland, Matthias and Basran, Parminder S. and Birman, Ken},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {23510-23511},
  doi       = {10.1609/AAAI.V38I21.30450},
  url       = {https://mlanthology.org/aaai/2024/gupta2024aaai-digital/}
}