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<p>Modern dairy farming and increasing cattle herd sizes are economic challenges that require cost-effective and accurate solutions that ensure high fertility. This can be achieved by early and reliable detection of heat phenomena in the estrous cycle. Traditional observation-based methods present significant challenges such as observer fatigue and variability in observer expertise. In addition, recent methods proposed for heat detection are often insufficient due to high costs or limited reliability. To overcome these issues, we proposed a new system called <i>CowStrus</i> that overcomes the limitations of existing methods by exploiting the overhead view using surveillance cameras fixed to the ceiling of the barn or surveillance drones. The captured videos are processed using an enhanced version of YOLOv8, fine-tuned through transfer learning to optimize performance in detecting cows and accurately identifying their heads. This information is used to extract spatiotemporal features, which are then used by a decision tree-based classifier to ensure proper detection of cows in heat. Decision making is performed based on a cumulative score of the classifications of sexual activities observed in cows. Evaluation of the system on real images showed promising results highlighting the potential of <i>CowStrus</i> as a reliable and economically viable solution, achieving high scores in four classes of sexual activity: F-Score of 0.965 for the <i>Cajoling</i> class, 0.897 for <i>Sniffing</i>, 0.857 for <i>Resting_the_chin</i> and 1.000 for <i>Mounting</i>.</p>

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Enhancing estrus detection in dairy cows: a cumulative scoring model using sexual activity classification

  • 期刊论文

  • 发表日期：

    2026年1月30日

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  • Ali Benyounes,
  • Karima Benhamza,
  • Brahim Farou,
  • Lynda Djakhdjakha,
  • Hamid Seridi

摘要

Modern dairy farming and increasing cattle herd sizes are economic challenges that require cost-effective and accurate solutions that ensure high fertility. This can be achieved by early and reliable detection of heat phenomena in the estrous cycle. Traditional observation-based methods present significant challenges such as observer fatigue and variability in observer expertise. In addition, recent methods proposed for heat detection are often insufficient due to high costs or limited reliability. To overcome these issues, we proposed a new system called CowStrus that overcomes the limitations of existing methods by exploiting the overhead view using surveillance cameras fixed to the ceiling of the barn or surveillance drones. The captured videos are processed using an enhanced version of YOLOv8, fine-tuned through transfer learning to optimize performance in detecting cows and accurately identifying their heads. This information is used to extract spatiotemporal features, which are then used by a decision tree-based classifier to ensure proper detection of cows in heat. Decision making is performed based on a cumulative score of the classifications of sexual activities observed in cows. Evaluation of the system on real images showed promising results highlighting the potential of CowStrus as a reliable and economically viable solution, achieving high scores in four classes of sexual activity: F-Score of 0.965 for the Cajoling class, 0.897 for Sniffing, 0.857 for Resting_the_chin and 1.000 for Mounting.