Papers
2
Total Citations
48
H-Index
2
About
Yu-Bo Sheng is a leading researcher at the intersection of agricultural automation and surgical robotics, with key contributions in deep learning-based edge detection and adaptive teleoperation control. His most cited work, "Edge Device Detection of Tea Leaves with One Bud and Two Leaves Based on ShuffleNetv2-YOLOv5-Lite-E" (2023, 41 citations), addresses a critical challenge in precision agriculture by enabling tea-picking robots to accurately identify harvest-ready leaves in real time. By replacing conventional feature extraction networks with a lightweight ShuffleNetv2 backbone, Sheng’s method achieves efficient deployment on edge devices, significantly advancing the feasibility of automated tea harvesting. In the medical domain, his paper "Teleoperated Surgical Robot with Adaptive Interactive Control Architecture for Tissue Identification" (2023, 7 citations) introduces a novel physical human-robot interaction interface that enhances a surgeon’s remote tactile perception. This adaptive framework allows surgical robots to identify tissue properties during teleoperation, improving safety and precision in minimally invasive procedures. Sheng’s work demonstrates a rare versatility, applying cutting-edge AI and control theory to solve real-world problems in both agriculture and healthcare. His research has garnered growing attention, with cumulative citations reflecting its practical impact on robotic perception and autonomy.
Research Focus
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Top Papers
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