From predictive analytics to emotional recognition–The evolving landscape of cognitive computing in animal welfare

Abstract

This paper explores the fusion of data science and cognitive techniques in deciphering the behaviors and emotions of farm animals. The focus is on the strategic application of digital imaging and artificial intelligence to discern subtle behavioral patterns and micro-expressions in livestock, offering a predictive window into their emotional states. The significance of acoustic vocalization analysis in interpreting complex communicative signals and emotional subtleties is highlighted. The work extends to cognitive evaluations, such as mirror tests and bias assessments, revealing higher levels of self-awareness and cognitive abilities in farm animals than previously recognized. Emphasizing the need for a synergistic approach, the paper advocates for melding technological advancements with a deep understanding of animal psychology and behavior. This ensures that technology enhances rather than supplants traditional observational methods in animal welfare. The discussion delves into various methodologies and algorithms that measure cognition, underscoring the pivotal role of cognitive computing in advancing animal welfare. A cautious and informed application of these technologies is proposed, emphasizing their role in augmenting, not undermining, the essential human-animal bond. Ultimately, this critical review calls for an ethical, empathetic, and scientifically grounded integration of cognitive computing into animal welfare practices.