Xianlu Guan
Papers
2
Total Citations
41
H-Index
2
About
Xianlu Guan is a researcher at the forefront of agricultural artificial intelligence, specializing in deep learning, computer vision, and precision agriculture. Their major contributions lie in developing lightweight, efficient models for real-time field applications, addressing the critical challenge of deploying AI on resource-constrained agricultural equipment. Guan’s most cited work, "GTCBS-YOLOv5s" (2023, 32 citations), introduces a streamlined object detection model for weed species identification in paddy fields, significantly reducing computational load while maintaining high accuracy—a breakthrough for sustainable weed management. Building on this, "Pomelo-Net" (2024, 9 citations) presents a semantic segmentation model tailored for honey pomelo orchards, enabling automated navigation by accurately identifying key environmental elements. These innovations demonstrate Guan’s expertise in balancing model performance with practical deployability, directly supporting the advancement of autonomous farming systems. Their work is widely recognized for bridging the gap between cutting-edge AI and real-world agricultural challenges, making Guan a notable figure in the field of smart farming and precision agriculture.
Research Focus
Key Achievements
Top Papers
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