Ziteng Xu

University of Missouri, Texas A&M University

Papers

Total Citations

H-Index

About

Ziteng Xu is a precision livestock farming researcher whose work focuses on automated reproductive monitoring in sows using advanced robotics and artificial intelligence. Their key research areas include computer vision, deep learning, and robotic imaging systems applied to animal behavior and physiology. Xu’s major contributions center on developing non-invasive, automated methods for estrus detection—a critical factor in optimizing sow reproductive performance. Their most cited paper, "Posture identification for stall-housed sows around estrus using a robotic imaging system" (2023, 7 citations), pioneered the use of robotic cameras to identify behavioral cues associated with estrus. Building on this, Xu’s 2024 study introduced a fully automated estrus detection method that replaces the labor-intensive back-pressure test, significantly improving accuracy and efficiency. More recently, Xu developed a novel pipeline combining LiDAR imagery and deep learning to quantify sow vulva volume changes—a key biological sign of estrus—demonstrating how AI can capture subtle physiological indicators. With a growing citation record and a clear trajectory toward practical, scalable solutions for swine reproduction management, Xu’s work represents a meaningful step toward reducing labor costs and improving conception rates in commercial pig farming.