Fuxi Shi
Papers
2
Total Citations
156
H-Index
2
About
Fuxi Shi is a leading researcher in agricultural robotics and computer vision, with a primary focus on precision pollination for kiwifruit orchards. Their work centers on developing real-time, deep-learning-based detection systems that enable robots to identify and target individual flowers and buds simultaneously—a critical step toward automating the labor-intensive process of kiwifruit pollination. Shi’s major contributions include pioneering the use of YOLOv4 and YOLOv5l architectures for multi-class flower detection, achieving high accuracy in complex orchard environments. Their 2021 paper on YOLOv4-based detection has garnered 97 citations, while their subsequent 2022 study on YOLOv5l and Euclidean distance mapping for distribution identification has earned 59 citations, reflecting the field’s strong interest in scalable, real-time solutions. By integrating object detection with spatial analysis, Shi has provided a foundational framework for robotic pollination systems that can operate efficiently in dynamic outdoor settings. Their work not only advances agricultural automation but also addresses the growing need for sustainable pollination methods in the face of declining natural pollinators.
Research Focus
Key Achievements
Top Papers
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