Jinhong Lv
Papers
4
Total Citations
34
H-Index
3
About
Jinhong Lv is at the forefront of agricultural robotics, pioneering intelligent systems that bridge the gap between computer vision and autonomous harvesting. His research centers on three critical pillars: robotic end-effector design, semantic perception for agricultural environments, and precise plant organ segmentation. Lv’s most impactful work, a comprehensive review on fruit- and vegetable-picking robot end-effectors (18 citations), establishes foundational design principles for Agriculture 4.0. He has made significant contributions to tea harvesting automation, developing a segmentation network that handles the complex challenge of multi-shape tea bud leaves (7 citations) and an instance segmentation system that achieves 3D pose estimation of tea bud leaves for autonomous harvesters (2 citations). Notably, Lv introduced MOLO-SLAM (7 citations), a semantic SLAM framework that robustly removes dynamic objects in agricultural settings—a breakthrough for reliable robot navigation in unstructured environments. His work directly addresses real-world agricultural challenges, from occlusion handling to scale-invariant detection, with demonstrated impact in both peer-reviewed citations and practical automation applications. Lv’s research is essential reading for anyone working at the intersection of robotics, computer vision, and precision agriculture.
Research Focus
Key Achievements
Top Papers
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