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Related Experiment Video

Updated: Jul 2, 2026

Murine Drinking Models in the Development of Pharmacotherapies for Alcoholism: Drinking in the Dark and Two-bottle Choice
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Analysis of the Drinking Behavior of Beef Cattle Using Computer Vision.

Md Nafiul Islam1, Jonathan Yoder1, Amin Nasiri1

  • 1Department of Biosystems Engineering and Soil Science, University of Tennessee, Knoxville, TN 37996, USA.

Animals : an Open Access Journal From MDPI
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a computer vision system to monitor beef cattle drinking behavior, improving animal health monitoring in livestock farming. The system achieved 97.35% accuracy in identifying drinking periods.

Keywords:
animal behaviorbeef cattlecomputer visiondrinking timeprecision livestock farming

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Area of Science:

  • Animal Science
  • Computer Vision
  • Machine Learning

Background:

  • Monitoring animal drinking behavior is crucial for livestock health and well-being.
  • Traditional methods for measuring drinking time are labor-intensive and challenging for large-scale livestock production.
  • Computer vision offers a potential solution for automated and efficient monitoring.

Purpose of the Study:

  • To develop a computer vision system for monitoring beef cattle drinking behavior.
  • To utilize low-cost camera systems for data acquisition.
  • To enable automated analysis of animal welfare through behavioral monitoring.

Main Methods:

  • Developed a data acquisition system with an RGB camera and ultrasonic sensor.
  • Employed DeepLabCut, a deep learning architecture, for tracking key cattle body parts (head-ear-neck).
  • Utilized a long short-term memory (LSTM) model to classify drinking and non-drinking periods from extracted key points.

Main Results:

  • The developed system accurately tracked beef cattle key body parts.
  • The LSTM model achieved 97.35% accuracy in classifying drinking and non-drinking periods during testing.
  • A total of 70 videos were used for training and testing, with 8 for validation.

Conclusions:

  • The computer vision system effectively monitors beef cattle drinking behavior.
  • This technology can enhance farmers' capabilities in monitoring animal health and well-being.
  • The findings address immediate needs in livestock farming for efficient behavioral monitoring.