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Home / Equine Research Bank / Sensors (Basel) / Sleeping and Eating Behavior Recognition of Horses Based on ...
Sensors (Basel, Switzerland)2024; 24(23); 7791; doi: 10.3390/s24237791

Sleeping and Eating Behavior Recognition of Horses Based on an Improved SlowFast Network.

Abstract: The sleeping and eating behaviors of horses are important indicators of their health. With the development of the modern equine industry, timely monitoring and analysis of these behaviors can provide valuable data for assessing the physiological state of horses. To recognize horse behaviors in stalls, this study builds on the SlowFast algorithm, introducing a novel loss function to address data imbalance and integrating an SE attention module in the SlowFast algorithm's slow pathway to enhance behavior recognition accuracy. Additionally, YOLOX is employed to replace the original target detection algorithm in the SlowFast network, reducing recognition time during the video analysis phase and improving detection efficiency. The improved SlowFast algorithm achieves automatic recognition of horse behaviors in stalls. The accuracy in identifying three postures-standing, sternal recumbency, and lateral recumbency-is 92.73%, 91.87%, and 92.58%, respectively. It also shows high accuracy in recognizing two behaviors-sleeping and eating-achieving 93.56% and 98.77%. The model's best overall accuracy reaches 93.90%. Experiments show that the horse behavior recognition method based on the improved SlowFast algorithm proposed in this study is capable of accurately identifying horse behaviors in video data sequences, achieving recognition of multiple horses' sleeping and eating behaviors. Additionally, this research provides data support for livestock managers in evaluating horse health conditions, contributing to advancements in modern intelligent horse breeding practices.
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Publication Date: 2024-12-05 PubMed ID: 39686329PubMed Central: PMC11645009DOI: 10.3390/s24237791Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research paper presents an improved SlowFast algorithm for the automatic recognition of horses’ sleeping and eating behaviors. The algorithm delivers a high degree of accuracy and can be used as a tool for assessing the health of horses in a modern equine industry.

Introduction of the Improved SlowFast Algorithm

  • The authors emphasize the importance of monitoring the sleeping and eating behaviors of horses as these act as vital markers of their overall health.
  • In this research, they propose modifications to the SlowFast algorithm to enhance its accuracy in recognizing horse behaviors. This advanced version is intended to be used in automating such recognition in video data sequences.
  • The improved SlowFast network algorithm employs the YOLOX model which proves instrumental in enhancing detection efficiency, thereby speeding up the video analysis process.

Loss Function and SE Attention Module Integration

  • The researchers devise a new loss function that aims to tackle data imbalance issues, a prevalent challenge in many machine learning problems.
  • They then integrate an SE attention module in the slow pathway of the SlowFast algorithm, enhancing its ability to focus on significant features related to horse behaviors.

Accuracy and Efficiency

  • The accuracy achieved in detecting the three postures of standing, sternal recumbency, and lateral recumbency is reported to be 92.73%, 91.87%, and 92.58%, respectively.
  • The system also demonstrates high accuracy in distinguishing between sleeping and eating behaviors, achieving scores of 93.56% and 98.77%.
  • The best overall accuracy reached by the model is 93.90%.

Significance and Impact of the Research

  • The horse behavior recognition method, based on the revised SlowFast algorithm, is capable of accurately identifying multiple horses’ behaviors from video sequences.
  • This will be extremely helpful for ranch managers in assessing horse health conditions, thus contributing to the modernization of horse breeding practices.
  • The research also opens up possibilities for the algorithm to be adapted for use in monitoring the behaviors of other livestock species.

Cite This Article

APA
Liu Y, Zhou F, Zheng W, Bai T, Chen X, Guo L. (2024). Sleeping and Eating Behavior Recognition of Horses Based on an Improved SlowFast Network. Sensors (Basel), 24(23), 7791. https://doi.org/10.3390/s24237791

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 24
Issue: 23
PII: 7791

Researcher Affiliations

Liu, Yanhong
  • College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
  • Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100080, China.
  • Xinjiang Agricultural Informatization Engineering Technology Research Center, Urumqi 830052, China.
  • Ministry of Education Engineering Research Centre for Intelligent Agriculture, Urumqi 830052, China.
Zhou, Fang
  • College of Information Science and Technology, Shihezi University, Shihezi 832000, China.
Zheng, Wenxin
  • Institute of Animal Husbandry Quality Standards, Xinjiang Academy of Animal Science, Urumqi 830011, China.
Bai, Tao
  • College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
  • Xinjiang Agricultural Informatization Engineering Technology Research Center, Urumqi 830052, China.
  • Ministry of Education Engineering Research Centre for Intelligent Agriculture, Urumqi 830052, China.
Chen, Xinwen
  • Institute of Animal Husbandry Quality Standards, Xinjiang Academy of Animal Science, Urumqi 830011, China.
Guo, Leifeng
  • Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100080, China.

MeSH Terms

  • Horses / physiology
  • Animals
  • Sleep / physiology
  • Algorithms
  • Feeding Behavior / physiology
  • Behavior, Animal / physiology
  • Video Recording
  • Posture / physiology

Grant Funding

  • ZZZC202112 / the University-Level Project of Shihezi University
  • 2022B02027, 2023B02013 / Key R&D Program of Xinjiang Uygur Autonomous Region
  • CXFZ202101 / the Innovation and Development Special Project of Shihezi University

Conflict of Interest Statement

The authors declare no conflicts of interest.

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