Papers
2
Total Citations
14
H-Index
2
About
Jipeng Ni is a leading researcher in agricultural robotics, with a focused expertise in computer vision and precision manipulation for automated fruit harvesting. His work centers on solving the critical challenge of non-destructive tomato picking, where a robotic arm must approach and grasp delicate fruit with the correct pose without causing damage. Ni’s major contributions include the development of **TomatoPoseNet**, an efficient keypoint-based 6D pose estimation model that enables robots to accurately perceive the orientation of tomatoes in cluttered, real-world environments. He also pioneered a semantic segmentation-based observation pose estimation method, which has already garnered 9 citations since its 2025 publication. These innovations directly address the difficulties posed by small, occluded pedicels and complex field conditions. With a combined citation count of 14 for his most-cited works, Ni’s research is rapidly gaining recognition for its practical impact on agricultural automation. His work not only advances robotic perception but also paves the way for more gentle, efficient, and commercially viable harvesting systems, marking him as a rising authority in the intersection of deep learning and agricultural robotics.
Research Focus
Key Achievements
Top Papers
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