Youjun Yue
Papers
4
Total Citations
65
H-Index
3
About
Youjun Yue’s research lies at the intersection of robotics, computer vision, and intelligent agriculture, with a focus on enabling autonomous systems to perceive and navigate complex environments. Their most cited work, “An Improved Ant Colony Algorithm of Robot Path Planning for Obstacle Avoidance” (2019, 29 citations), tackles a critical challenge in mobile robot control by enhancing path planning in narrow spaces, reducing computational load, and improving obstacle avoidance—a foundational contribution to autonomous navigation. Yue also advances agricultural automation through deep learning: their 2020 study on fruit disease detection using Mask R-CNN (28 citations) achieves high-speed, accurate identification of disease spots on apples, peaches, and more, addressing key bottlenecks in quality sorting. In night-vision imaging for harvesting robots, Yue developed an improved FastICA denoising method (2017) to filter Gaussian and salt-and-pepper noise, boosting precision in low-light conditions. More recently, they applied generative adversarial networks to semantically segment freshwater fish bodies (2020), enabling precise robotic grasping and cutting. With a growing citation impact, Yue’s work bridges algorithmic innovation and real-world agricultural robotics, offering practical solutions for automation in challenging settings.
Research Focus
Key Achievements
Top Papers
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