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About

Yongjun Zheng is a leading researcher at the intersection of agricultural robotics, precision measurement, and human-robot interaction. His work centers on developing intelligent sensing and automation systems for agricultural environments, with a particular focus on laser-based measurement, machine vision, and digital twin technologies. Zheng’s most impactful contribution is his pioneering work on a UGV-mounted laser scanner system for measuring tree geometric characteristics (2013, 7 citations), which established a foundational method for non-contact, automated crop phenotyping. He has also advanced the field of plant factory automation through his research on digital twins and data-driven vibration monitoring for transplanters (2023, 6 citations), enabling real-time quality analysis that directly improves seedling survival and yield. In the domain of harvesting robotics, Zheng developed innovative walking goal line detection algorithms using machine vision and improved Hough transforms (2011, 2012), addressing real-time processing challenges for autonomous navigation. More recently, he has explored virtual reality platforms to enhance the fluidity of human-robot interaction (2024). With a career spanning over a decade, Zheng’s work bridges precision agriculture and robotics, offering practical solutions for sustainable, automated farming.