Papers
1
Total Citations
17
H-Index
1
About
Yiqun Wang is a researcher working at the intersection of computer vision and agricultural technology, with a focused expertise in deep learning-based object detection and instance segmentation. Wang's most notable contribution to date is the development of Y-HRNet, an innovative model that fuses the powerful YOLOv7 architecture with High-Resolution Network (HRNet) to achieve precise multi-category instance segmentation of cherry tomatoes. Published in 2024, this work addresses a critical challenge in precision agriculture: accurately identifying and distinguishing individual fruit instances across varying ripeness stages and complex visual environments. The model demonstrates Wang's ability to synthesize state-of-the-art deep learning frameworks into practical agricultural applications, contributing meaningfully to the advancement of automated crop monitoring and robotic harvesting systems. With 17 citations already accumulated, this early-career work has attracted notable attention from both the computer vision and agri-tech research communities, signaling strong relevance to ongoing efforts in smart farming. Wang's research represents a growing and important bridge between artificial intelligence innovation and real-world agricultural problem-solving.
Research Focus
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Top Papers
- 1