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About

Fengying Dang is a versatile researcher whose work spans two distinct but equally impactful domains: precision agriculture and autonomous underwater robotics. In agricultural technology, Dang has emerged as a leading figure in applying deep learning-based object detection to weed management, developing benchmark datasets and evaluation frameworks for YOLO-based models in cotton production systems. The landmark study *YOLOWeeds* (2023) has already garnered an impressive 271 citations, reflecting its rapid adoption by the computer vision and agricultural engineering communities as a foundational resource for multi-class weed detection research. This work directly addresses the critical challenge of reducing herbicide dependence by enabling intelligent, vision-guided precision weed control. Earlier in Dang's career, their research focused on the sensing and navigation of bio-inspired underwater robots, contributing novel approaches to hydrodynamic coefficient identification and distributed flow estimation using advanced techniques such as Proper Orthogonal Decomposition and Dynamic Mode Decomposition paired with Bayesian filtering. These contributions provided essential tools for improving the autonomy and environmental awareness of robotic fish systems. Taken together, Dang's body of work demonstrates a rare breadth of expertise, combining robotics, fluid dynamics, and machine vision to tackle real-world challenges in both aquatic environments and modern agricultural systems.