Bo Xuan Gu
Papers
1
Total Citations
22
H-Index
1
About
Bo Xuan Gu is a researcher whose work sits at the intersection of computer vision and agricultural automation, with a particular focus on lightweight deep learning models for precision farming. His most notable contribution is the development of YOLO-DCA, an improved citrus detection model based on YOLOv7-tiny, designed to overcome the challenges of complex orchard environments—including variable lighting, branch occlusion, and fruit overlap. By integrating depth-separable convolution (DWConv), Gu achieved a significant reduction in model complexity while maintaining high detection accuracy, making real-time deployment on resource-constrained devices feasible. His 2023 paper on this work has already garnered 22 citations, reflecting its immediate relevance to the growing field of smart agriculture. Gu’s research addresses a critical bottleneck in automated fruit harvesting and yield estimation, offering practical solutions that balance computational efficiency with robust performance. His work is particularly valuable for researchers and engineers developing edge-AI systems for agricultural robotics, and it stands as a model for how lightweight architectures can be adapted to domain-specific visual tasks.
Research Focus
Key Achievements
Top Papers
- 1