Xiaoxue Guo

Papers

Total Citations

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About

Xiaoxue Guo is a rising researcher at the intersection of computer vision and agricultural automation, with a primary focus on deep learning-driven object detection for precision agriculture. Her most cited work introduces an improved YOLOv8 neural network model for fresh tea leaf grading detection, a critical step toward fully automated tea harvesting. By integrating a Hierarchical Vision Transformer using Shifted Windows (Swin Transformer) into the YOLOv8 architecture, Guo’s approach significantly enhances both the speed and accuracy of real-time tea leaf classification, addressing a long-standing bottleneck in the tea industry. Although published in 2024 and already garnering 5 citations, this paper demonstrates immediate impact by offering a practical, deployable solution for smart agriculture. Guo’s contributions lie in bridging advanced vision transformer techniques with traditional agricultural tasks, showcasing how state-of-the-art AI can be tailored for field-specific challenges. Her work not only advances the field of agricultural robotics but also provides a scalable framework for grading other perishable crops, marking her as a promising innovator in applied deep learning.