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Hot topic: Detecting digital dermatitis with computer vision.

Preston Cernek1, Nathan Bollig2, Kelly Anklam1

  • 1Department of Medical Sciences, School of Veterinary Medicine, University of Wisconsin, Madison 53706.

Journal of Dairy Science
|August 31, 2020
PubMed
Summary
This summary is machine-generated.

A new computer vision (COMV) tool accurately detects digital dermatitis (DD) in cattle. This technology aids early DD identification, potentially reducing disease prevalence and improving herd health.

Keywords:
Computer visiondigital dermatitismachine learning

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Area of Science:

  • Veterinary Medicine
  • Artificial Intelligence
  • Animal Science

Background:

  • Digital dermatitis (DD) significantly impacts cattle health, causing lameness, infertility, and reduced milk yield.
  • Early DD detection is crucial for effective treatment but remains challenging in commercial dairy settings.
  • Computer vision (COMV) offers a promising technological solution for automated DD identification.

Purpose of the Study:

  • To develop and implement an innovative COMV tool for identifying digital dermatitis lesions in cattle.
  • To train a COMV model using the YOLOv2 architecture for detecting M-stages of DD.
  • To validate the COMV model's performance in detecting DD on a commercial dairy farm.

Main Methods:

  • A dataset of over 3,500 digital dermatitis lesion images was utilized.
  • The YOLOv2 architecture was employed to train a COMV model for DD detection.
  • Internal and external validation methods were used to assess model accuracy and agreement with human evaluation.

Main Results:

  • The YOLOv2 COMV model achieved 71% accuracy in internal validation, with moderate agreement (Cohen's kappa) compared to human evaluators.
  • External validation demonstrated an improved accuracy of 88% for the COMV model, with fair agreement (Cohen's kappa).

Conclusions:

  • The developed COMV tool shows potential for accurate digital dermatitis detection in cattle.
  • Implementing COMV for DD detection can facilitate timely treatment, lower disease prevalence, and enhance animal welfare.
  • This technology represents a significant advancement in managing cattle health on commercial dairy farms.