Papers
3
Total Citations
104
H-Index
3
About
Yuhuan Sun is a rising researcher at the forefront of agricultural robotics and intelligent perception, whose work is shaping the future of precision farming. Sun’s primary research areas span instance segmentation, multimodal sensor fusion, and robotic hand-eye coordination for complex agronomic tasks. In a landmark contribution, Sun developed MTA-YOLACT, a multitask-aware network for fruit bunch identification in cherry tomato robotic harvesting, which has garnered 50 citations for its practical impact on automated agriculture. Complementing this, Sun introduced YOLACTFusion, an innovative RGB-NIR multimodal image fusion method enhanced by attention mechanisms, earning 47 citations and advancing robust perception under variable lighting conditions. Most recently, Sun authored a comprehensive review on agricultural robot hand-eye coordination, synthesizing strategies for integrating vision and manipulation in agronomic tasks. This work, already cited 7 times, critically examines emerging trends and challenges in deploying robots for complex field operations. Through these contributions, Sun has demonstrated a clear trajectory of innovation, bridging deep learning with real-world agricultural automation. With a growing citation footprint and a focus on solving tangible problems in food production, Yuhuan Sun is a researcher to watch in the dynamic intersection of computer vision and agricultural robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3