Zhenwei Xing

Papers

Total Citations

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About

Zhenwei Xing is a researcher at the forefront of agricultural robotics and computer vision, with a focus on precision detection and segmentation of specialty crops. His work addresses critical challenges in automated harvesting, particularly for structurally complex plants like jujube trees and wolfberry bushes. Xing’s major contribution lies in developing lightweight, high-performance deep learning architectures tailored for natural, unstructured agricultural environments. His most cited paper, “Object Detection Algorithm for Lingwu Long Jujubes Based on the Improved SSD” (2022, 16 citations), introduces a streamlined single shot multi-box detector that balances computational efficiency with detection accuracy, enabling real-time robotic picking. Building on this, his work “MFENet: Multi-scale feature extraction network for images deblurring and segmentation of swinging wolfberry branch” (2023, 11 citations) tackles the dual challenge of motion blur and precise segmentation in dynamic field conditions. By integrating multi-scale feature extraction, MFENet significantly enhances the robustness of vision systems for swinging branches—a common yet difficult scenario in agricultural robotics. Xing’s research directly supports the development of intelligent, low-cost harvesting robots, making him a key contributor to the growing field of smart agriculture.