Papers
4
Total Citations
22
H-Index
3
About
Licheng Zhu is a robotics researcher whose work is advancing the field of agricultural automation, with a particular focus on intelligent perception for fruit harvesting. His primary research areas include computer vision, semantic segmentation, and 6D pose estimation for robotic manipulation in complex agricultural environments. Zhu’s major contributions lie in developing deep learning models that enable robots to non-destructively harvest delicate crops like tomatoes. His most cited work, “Semantic segmentation-based observation pose estimation method for tomato harvesting robots” (2025, 9 citations), addresses the critical challenge of guiding a robotic arm to approach fruit with the correct posture. He further refined this with “TomatoPoseNet” (2024, 5 citations), an efficient keypoint-based model designed to overcome difficulties posed by small pedicels and cluttered settings. Zhu has also tackled environmental variability, as seen in “Parallel RepConv network” (2025, 5 citations), which improves obstacle detection under diverse lighting conditions in vineyards. His research trajectory, from early work on SIFT-based object matching (2011, 3 citations) to cutting-edge neural architectures, demonstrates a sustained commitment to making agricultural robots more reliable and effective in real-world conditions, directly impacting the future of precision farming.
Research Focus
Key Achievements
Top Papers
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- 4SIFT-based algorithm for object matching and identification3 citations · 2011