Papers
2
Total Citations
21
H-Index
2
About
Xing Sun is a researcher at the forefront of agricultural robotics and computational imaging, whose work bridges the gap between advanced machine learning and real-world application. His primary research areas include precision agriculture, computer vision, and deep learning, with a particular focus on automating fruit harvesting in complex natural environments. Sun’s most notable contribution is the development of the Litchi-YOSO model, a lightweight yet highly effective segmentation algorithm that accurately identifies litchi fruit and branches in cluttered orchard settings. This model, combined with a novel branch morphology reconstruction algorithm, achieves a 91.5% success rate in locating optimal picking points, a critical step toward fully autonomous harvesting. The model’s impressive balance of performance (76.45% [email protected]) and efficiency (29.1 MB, 26.5 ms per image) makes it a practical solution for field deployment. In addition to his agricultural work, Sun has made significant strides in computational imaging, proposing a deep nonparametric Bayesian method to generate light fields from a single image—a technique that overcomes traditional trade-offs between angular and spatial resolution. With over 20 citations across his most-cited works, Xing Sun is establishing himself as a key innovator in intelligent agricultural systems and computational optics.
Research Focus
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Top Papers
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