Papers
4
Total Citations
15
H-Index
2
About
Zhenhua Tan is a pioneering researcher at the intersection of agricultural robotics and soft sensing, whose work is redefining how robots interact with delicate produce. His primary research areas include soft robotic grippers, intelligent fruit harvesting, and sensor-based ripeness detection. Tan’s most notable contribution is the development of a hand-like gripper embedded with a flexible gel sensor for tomato harvesting, enabling soft contact and real-time ripeness assessment—a breakthrough that has already garnered 7 citations since its 2025 publication. He has also advanced mobile robot navigation through a novel loop closure detection method combining residual and capsule networks, achieving 5 citations for improving SLAM robustness in complex environments. Additionally, Tan’s micro-modelling of tomato pericarp microstructure and mechanical properties provides a force control reference for harvesting robots, helping to prevent internal fruit damage. With a total of 15 citations across his key works, Tan’s research is laying the groundwork for more intelligent, gentle, and efficient automated harvesting systems, promising to significantly reduce post-harvest losses in agriculture.
Research Focus
Key Achievements
Top Papers
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