Shangjie Xie

Papers

Total Citations

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About

Shangjie Xie is a researcher at the forefront of agricultural robotics and computer vision, with a primary focus on intelligent fruit detection and automated harvesting systems. His most cited work, "Grape Maturity Detection and Visual Pre-Positioning Based on Improved YOLOv4" (2022, 28 citations), addresses a critical challenge in precision agriculture: enabling robots to accurately recognize and classify grape clusters by maturity in complex orchard environments. By enhancing the YOLOv4 deep learning architecture, Xie developed an algorithm that not only distinguishes between ripe and unripe grapes but also extracts spatial positioning data, providing the visual guidance necessary for robotic picking arms to operate effectively. This contribution bridges the gap between machine perception and physical manipulation, offering a practical solution for reducing labor costs and improving harvest efficiency. Xie’s research stands out for its integration of real-time detection with pre-positioning, a dual capability that is essential for non-destructive fruit handling. His work is a valuable resource for students and researchers exploring the intersection of deep learning, robotics, and sustainable agriculture, demonstrating how advanced computer vision can transform traditional farming into a data-driven, automated industry.