Beom Sahng Ryuh

Jeonbuk National University, Korea Automotive Technology Institute

Papers

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About

Beom Sahng Ryuh is a researcher whose work sits at the intersection of computer vision, machine learning, and agricultural robotics, with a particular focus on advancing automation in dairy farming through intelligent sensing systems. His most recognized contribution involves the development and comparative evaluation of teat detection algorithms for robotic milking systems, most notably his 2019 study benchmarking YOLO against Haar-cascade methods — a paper that has garnered 21 citations and become a reference point for researchers working on precision livestock technology. Building on this, Ryuh has consistently pushed the boundaries of smart Automatic Milking Systems (AMS), exploring the integration of Time-of-Flight (TOF), RGB-D, and thermal imaging technologies to enable faster, more accurate teat localization by robotic manipulators. His earlier 2017 studies laid the conceptual and experimental groundwork for next-generation milking robots capable of operating with minimal human intervention. Ryuh's body of work reflects a dedicated effort to bridge laboratory innovation with real-world agricultural challenges, making meaningful contributions to the design of vision-guided robotic systems that could significantly improve efficiency and animal welfare in modern dairy operations.