Dengbin Fu
Papers
2
Total Citations
12
H-Index
2
About
Dengbin Fu is a researcher in agricultural robotics and computer vision, specializing in autonomous navigation for field robots. His work focuses on developing robust perception and control systems for paddy field environments, where traditional GPS-based methods often fail. Fu’s most-cited paper, "Vision-based trajectory generation and tracking algorithm for maneuvering of a paddy field robot" (2024, 10 citations), introduces a novel approach to real-time path planning and control using visual inputs, enabling precise maneuvering in unstructured, waterlogged fields. This work addresses critical challenges in agricultural automation, such as slippery terrain and variable lighting. His more recent contribution, "PRSGNet: A robust framework for crop row detection in complex field scenarios" (2025, 2 citations), presents a deep learning architecture that achieves high accuracy in detecting crop rows under occlusion, weed pressure, and varying growth stages. By combining vision-based trajectory generation with robust row detection, Fu’s research advances the practicality of autonomous paddy field robots, reducing reliance on manual labor and improving efficiency in rice cultivation. His work is gaining traction among researchers in precision agriculture and field robotics.
Research Focus
Key Achievements
Top Papers
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- 2