Zeren Yi
Papers
2
Total Citations
53
H-Index
2
About
Zeren Yi has made impactful contributions at the intersection of agricultural robotics and intelligent navigation systems. Their research focuses on two key areas: computer vision for automated harvesting and adaptive robot navigation in complex environments. Yi’s most cited work, “Lemon-YOLO: An efficient object detection method for lemons in the natural environment” (2021, 42 citations), addresses a critical challenge in agricultural automation—accurately detecting fruit under varying illumination, occlusion, and color similarity between lemons and foliage. This efficient detection method provides a foundational technology for automatic harvesting robots, demonstrating Yi’s ability to solve real-world agricultural problems with deep learning. In parallel, Yi developed a novel navigation algorithm for mobile robots using interval type-2 fuzzy neural network fitting Q-learning (IT2FNN-Q, 2019, 11 citations). This work advances robot autonomy in unknown environments by combining fuzzy reasoning with reinforcement learning, enabling adaptive decision-making. Together, these contributions highlight Yi’s dual expertise in vision-based perception and intelligent control, with direct applications in precision agriculture and autonomous robotics. Their work continues to inspire researchers seeking practical AI solutions for challenging outdoor environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2