ASAS-NANP Symposium: Mathematical modeling in animal nutrition: revolutionizing animal farming with artificial intelligence: trends, challenges, and opportunities.

Artificial intelligence (AI) can transform livestock farming as producers start using data-driven decisions in key areas, such as animal health, reproduction, behavior, nutrition, and production management. This review examines how AI technologies, like machine learning, computer vision, and sensor-based systems, help monitor and manage livestock more precisely, efficiently, and responsively. From early disease detection and estrus prediction to real-time behavior tracking and automated feeding systems, AI offers powerful tools for improving productivity, enhancing animal welfare, and supporting sustainable farm operations. Despite the promising technological advances, adopting AI in livestock systems comes with significant challenges. These include issues related to data quality and availability, model generalizability, infrastructure limitations, and ethical concerns involving data privacy and animal welfare. This review critically examines these obstacles and points out the need for robust, interpretable AI solutions that can adapt to specific farm conditions and offer meaningful explanations to end-users. Emerging trends like multimodal sensor fusion, digital twins, edge AI, and the integration of AI with genomics and climate data offer exciting possibilities for next-generation livestock management and smart farming systems. It is equally crucial to focus on human-centered design, participatory design, and group model-building approaches to ensure AI tools are accessible, trusted, and address the real needs of farmers and caregivers. This article explores AI's potential to change livestock farming while advocating for interdisciplinary collaboration, inclusive innovation, and responsible deployment. It synthesizes current applications, challenges, and research frontiers. Ultimately, AI's impact on animal agriculture depends on technical advancements as well as our ability to integrate these tools into systems that are biologically sound, socially accepted, and ethically responsible.