Integrating Deep Learning and Transcriptomics to Assess Livestock Aggression: A Scoping Review - Roland Juhos; Szilvia Kusza; Vilmos Bilicki; Zoltán Bagi (Biology, 2025)
The presence of aggressive behavior in livestock creates major difficulties for animal welfare, farm safety, economic performance and selective breeding. The two innovative tools of deep learning-based video analysis and transcriptomic profiling have recently appeared to aid the understanding and monitoring of such behaviors. This scoping review assesses the current use of these two methods for aggression research across livestock species and identifies trends while revealing unaddressed gaps in existing literature. A scoping literature search was performed through the PubMed, Scopus and Web of Science databases to identify articles from 2014 to April 2025. The research included 268 original studies which were divided into 250 AI-driven behavioral phenotyping papers and 18 transcriptomic investigations without any studies combining both approaches. Most research focused on economically significant species, including pigs and cattle, yet poultry and small ruminants, along with camels and fish and other species, received limited attention. The main developments include convolutional neural network (CNN)-based object detection and pose estimation systems, together with the transcriptomic identification of molecular pathways that link to aggression and stress. The main barriers to progress in the field include inconsistent behavioral annotation and insufficient real-farm validation together with limited cross-modal integration. Standardized behavior definitions, together with multimodal datasets and integrated pipelines that link phenotypic and molecular data, should be developed according to our proposal. These innovations will speed up the advancement of livestock welfare alongside precision breeding and sustainable animal production.