International Symposium on Ruminant Physiology: Leveraging computer vision, large language models, and multimodal machine learning for optimal decision making in dairy farming.

This article explores various applications of artificial intelligence (AI) technologies in dairy farming, including the use of computer vision systems (CVS) for animal identification, BCS and body shape analysis, and potential uses of large language models (LLM) in the dairy industry. Among recent advancements in precision livestock farming tools, CVS have gained popularity as powerful solutions for individual animal monitoring. These systems can capture phenotypes from multiple animals simultaneously using a single device in an automated and nonintrusive manner. To match animals with their corresponding predicted phenotypes, these systems require individual animal identification, which can be achieved through external identification systems or computer vision-based animal identification algorithms. Additionally, modern natural language processing techniques, such as LLM, offer opportunities for advanced data integration, including unstructured textual data. Furthermore, we discuss the challenges associated with integrating data from different sources and modalities, such as images, text, and tabular data, into multimodal machine learning systems for phenotype prediction, which also represents a key area of AI application. Digital technologies such as CVS and LLM have the potential to transform dairy farming; CVS can provide individual and objective assessments of animal health, whereas LLM can integrate diverse data sources for phenotype prediction. Although there are many potential challenges ahead, these technologies offer significant opportunities for advancing animal health monitoring, farm management, and individual phenotyping.