Xingdong Sun

Papers

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About

Xingdong Sun is a leading researcher at the forefront of agricultural robotics and precision fruit harvesting. His work centers on developing advanced computer vision and deep learning systems to automate the detection and handling of orchard fruits, with a particular focus on apples. Sun’s major contributions involve significantly improving the speed and accuracy of fruit detection in unstructured, real-world environments. His highly cited 2024 paper on the YOLOv5s-GBR model, which has garnered 5 citations, introduced a novel approach to overcome the limitations of traditional, slow detection methods in orchards. Building on this, his 2025 work on an improved YOLOv8 architecture with dual cameras tackles the critical challenge of precise fruit stalk cutting, a key factor in preserving fruit quality during automated harvesting. This two-stage, two-camera system represents a notable achievement in robotic manipulation. With a growing citation impact, Sun’s research is directly shaping the future of smart agriculture, providing the foundational technology for efficient, damage-free robotic fruit picking.