Xiangli Meng
Papers
4
Total Citations
103
H-Index
3
About
Xiangli Meng is a leading researcher in agricultural robotics, specializing in computer vision and intelligent control for fruit-harvesting automation. Her work addresses critical challenges in precision agriculture, particularly the development of robust recognition and manipulation systems for picking robots. Meng’s most impactful contribution is a green pepper recognition method using a least-squares support vector machine optimized by improved particle swarm optimization, which solves the problem of distinguishing green peppers from similar-colored leaves—a paper cited 43 times. She also pioneered a branch localization technique for apple harvesting robots, employing skeleton feature extraction and stereo matching to enable obstacle avoidance, garnering 41 citations. Her research extends to fast segmentation of apple images under all-weather conditions using adaptive mean-shift and normalized cut methods, enhancing real-time performance for vision systems. Additionally, Meng has explored grasp force control for apple-picking robots through improved impedance control, ensuring delicate handling. With over 100 total citations, her work is foundational for advancing autonomous harvesting, directly impacting agricultural efficiency and reducing labor dependency.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4