Zhong Xue

Chinese Academy of Tropical Agricultural Sciences

Papers

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About

Zhong Xue is a researcher at the forefront of agricultural robotics and computer vision, with a primary focus on developing intelligent systems for mechanized fruit harvesting. Their major contributions center on creating lightweight, high-performance deep learning models specifically tailored for pineapple detection and segmentation in complex field environments. Xue’s most influential work, "MSGV-YOLOv7: A Lightweight Pineapple Detection Method" (2023), has garnered 13 citations and introduces a novel architecture that replaces traditional backbones with MobileOne and a thin neck network, significantly enhancing the speed and accuracy of pineapple recognition for harvesting robots. Building on this, their 2025 paper "Towards mechanized harvesting of pineapples: A masked self-attention instance segmentation network and pineapple detection dataset" (4 citations) advances the field by incorporating masked self-attention mechanisms for precise instance segmentation. This work also provides a dedicated pineapple detection dataset, addressing a critical gap in agricultural AI research. Xue’s achievements demonstrate a clear trajectory toward practical, deployable solutions for automated agriculture, with their lightweight models offering substantial improvements in computational efficiency without sacrificing detection performance—a key requirement for real-time robotic applications in the field.