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Affective State Recognition in Livestock-Artificial Intelligence Approaches.

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Monitoring farm animal welfare is crucial. This study proposes using artificial intelligence (AI) and biometric sensors to accurately measure animal emotions and improve welfare standards.

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affective statesanimal emotionsanimal welfareanimal-based measuresemotion modellingsensors

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Area of Science:

  • Animal Welfare Science
  • Artificial Intelligence in Agriculture
  • Computational Ethology

Background:

  • Over 70 billion farm animals globally face welfare challenges in intensive farming systems.
  • Growing evidence highlights animal suffering and complex emotional states, necessitating better welfare monitoring.
  • Current welfare assessments are often subjective, time-consuming, and lack objective measures for transient affective states.

Purpose of the Study:

  • To review innovative methods for collecting big data on farm animal emotions.
  • To explore the application of AI and sensor technology for quantifying affective states in livestock.
  • To propose advanced analytical techniques for understanding animal emotional dynamics.

Main Methods:

  • Utilizing biometric sensor data for unobtrusive monitoring of individual animals (pigs and cows).
  • Training artificial intelligence models to classify, quantify, and predict affective states.
  • Applying social network analysis to model emotional dynamics and contagion at the group level.
  • Exploring the concept of 'digital twins' for real-time simulation and prediction of animal affective states.

Main Results:

  • Proposed methods enable the collection of big data on farm animal emotions.
  • AI models can be trained to accurately assess individual animal affective states.
  • Social network analysis offers insights into group-level emotional contagion.
  • Digital twins present a near-term possibility for predictive welfare monitoring.

Conclusions:

  • AI-driven biometric monitoring offers a smart, efficient solution for assessing farm animal welfare.
  • Advanced data analysis techniques can provide objective, real-time insights into animal emotional states.
  • Future applications include predictive modeling and enhanced welfare management strategies for livestock.