Lie Tang
Papers
32
Total Citations
913
H-Index
15
About
Lie Tang is a pioneering researcher at the intersection of agricultural robotics, computer vision, and precision agriculture, whose work is reshaping how modern farming systems operate. Based on a body of highly cited scholarship, Tang's research focuses on three interconnected domains: autonomous agricultural vehicle navigation, robotic weed control, and field-based plant phenotyping. Among his most influential contributions is his development of computer vision systems that fuse color and depth imagery to detect and classify crop plants under real-world field conditions — work that has garnered over 110 citations and directly advances robotic weeding capabilities. His navigation research, including a Double-DQN-based path smoothing and tracking method (107 citations) and robust 4WD/4WS vehicle control, demonstrates his commitment to making agricultural robots practically deployable at scale. Tang has also made significant strides in automated phenotyping, applying stereo vision and deep convolutional neural networks to characterize morphological traits in sorghum, maize, and corn — accelerating breeding programs that would otherwise rely on slow, error-prone manual measurements. His UAV-based corn stand counting method further illustrates his range across sensing platforms. With cumulative citations exceeding 700, Tang's work is establishing the technological foundation for the next generation of intelligent, autonomous farming systems.
Research Focus
Key Achievements
Top Papers
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- 5Robust navigation control of a 4WD/4WS agricultural robotic vehicle65 citations · 2019
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- 8The use of agricultural robots in weed management and control47 citations · 2019
- 9A Robotic Platform for Corn Seedling Morphological Traits Characterization39 citations · 2017
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