Lingshen Fang
Chinese Academy of Sciences, Shenyang Institute of Automation
Papers
2
Total Citations
9
H-Index
2
About
Lingshen Fang is a researcher whose work bridges computer vision, robotics, and agricultural automation. Their key research areas include object recognition, robotic grasping, and machine learning applications in precision agriculture. Fang’s most cited paper, “A Method for Object Recognition and Robot Grasping Detection in Multi-object Scenes” (2022, 6 citations), advances the field of robotic manipulation by developing algorithms that enable robots to identify and grasp objects in cluttered environments—a critical challenge for industrial and service robotics. Another notable contribution, “Research on quality evaluation of maize seed shape based on support vector machine” (2016, 3 citations), applies machine learning to agricultural engineering. This work improves the efficiency of maize breeding by using an automatic laser cutting robot for seed sampling, with shape quality evaluation ensuring high automation and precision. By integrating SVM-based classification into robotic systems, Fang demonstrates how computational methods can enhance traditional agricultural processes. Though early in their career, Fang’s research shows promise in solving real-world problems at the intersection of robotics and agriculture, with potential applications in smart farming and automated manufacturing.
Research Focus
Key Achievements
Top Papers
- 1
- 2