Computer Vision-Based Detection of Agonistic Behaviors in Pigs: Advances and Applications for Precision Livestock Farming

Agonistic behaviors such as aggression, ear biting, and tail biting remain major challenges for pig welfare, particularly during the weaning and growing periods. Computer vision (CV) technologies are emerging as scalable tools for non-invasive monitoring of these behaviors. This systematic review summarizes recent advances in CV-based detection of agonistic behaviors in pigs and identifies factors influencing their reliability and commercial adoption. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, a structured search of Scopus, Web of Science, and PubMed identified 42 eligible studies. Most studies employ deep learning approaches, including you only look once (YOLO)-based detectors and spatio-temporal models, achieving detection accuracy of up to 97% for behaviors such as head knocking, head-to-body pushing, and tail biting, typically evaluated under controlled conditions using [email protected]. Three key findings emerged: rapid progress in deep learning-based detection; methodological heterogeneity in behavioral definitions, validation strategies, and annotation protocols; and a gap between high detection accuracy and demonstrated improvements in welfare or productivity. Progress is limited by scarce cross-farm validation, inconsistent bout definitions, reliance on manual annotations, and weak integration with physiological and production indicators. Future research should prioritize standardized behavioral definitions, multimodal integration, predictive modeling, and rigorous external validation.