Xiangqun Ren
Papers
1
Total Citations
5
H-Index
1
About
Xiangqun Ren is a leading researcher in agricultural robotics and computer vision, with a primary focus on precision automation for mushroom harvesting. Their most cited work, "Improved Real-Time Models for Object Detection and Instance Segmentation for Agaricus bisporus Segmentation and Localization System Using RGB-D Panoramic Stitching Images" (2024, 5 citations), addresses a critical bottleneck in automated harvesting: the accurate segmentation and localization of Agaricus bisporus mushrooms in dense, overlapping clusters. Ren’s major contribution lies in developing a low-cost, RGB-D panoramic stitching system that integrates real-time object detection and instance segmentation models, significantly improving the precision of identifying individual mushrooms even under challenging growth conditions. This innovation directly enables more reliable robotic harvesting, reducing damage and increasing efficiency. With 5 citations to date, Ren’s work is gaining traction among agricultural engineers and robotics researchers. Their achievements include pioneering the use of panoramic imaging combined with advanced deep learning architectures for fungal crop automation, a niche yet vital area for sustainable food production. Ren’s research holds promise for transforming labor-intensive mushroom farming into a fully automated, data-driven process.
Research Focus
Key Achievements
Top Papers
- 1