Yuning Xie
Papers
1
Total Citations
6
H-Index
1
About
Yuning Xie is a researcher focused on agricultural robotics and computer vision, with a particular emphasis on fruit detection in complex orchard environments. Their major contribution lies in developing advanced deep learning methods for automated fruit recognition, specifically addressing the challenge of detecting fruits with colors similar to their natural background—a persistent problem in precision agriculture. Xie’s most cited work, “Automatic detection of pecan fruits based on Faster RCNN with FPN in orchard” (2022), has garnered 6 citations, demonstrating its relevance in the field. This study introduced an innovative approach combining Faster R-CNN with Feature Pyramid Networks to improve detection accuracy under varying light conditions, a critical step toward enabling robotic harvesting. By tackling the understudied area of pecan fruit detection, Xie has laid groundwork for more efficient and reliable vision systems in orchard automation. Their research holds promise for reducing labor costs and increasing yield efficiency, making it a valuable resource for students and researchers exploring the intersection of computer vision and agricultural technology.
Research Focus
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Top Papers
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