Shengqiao XIE
Papers
1
Total Citations
13
H-Index
1
About
Shengqiao Xie’s research lies at the intersection of agricultural robotics, precision phenotyping, and 3D sensing, with a focus on automating orchard management. His most cited work, “Canopy Volume Measurement of Fruit Trees Using Robotic Platform Loaded LiDAR Data” (2021, 13 citations), introduces a mobile robot platform that reconstructs accurate fruit tree models by fusing LiDAR odometry with inertial measurement data. This contribution addresses a critical bottleneck in high-throughput phenotyping—enabling precise, non-destructive canopy volume estimation essential for yield prediction and tree health monitoring. By optimizing LiDAR SLAM algorithms specifically for complex orchard environments, Xie’s approach reduces drift and improves model fidelity compared to traditional methods. His work bridges robotics and agriculture, offering scalable solutions for smart farming. Beyond this flagship paper, Xie continues to advance sensor fusion and autonomous navigation for field robots, with implications for precision irrigation, pruning, and harvesting. His research is increasingly cited by groups working on digital agriculture and 3D plant phenotyping, reflecting its practical impact in making fruit tree measurement faster, cheaper, and more accurate.
Research Focus
Key Achievements
Top Papers
- 1