Quanfeng Guo
Papers
3
Total Citations
51
H-Index
3
About
Quanfeng Guo is a leading researcher in agricultural robotics, with a primary focus on the automated harvesting of safflower—a crop whose delicate filaments pose unique challenges for robotic vision and manipulation. His work centers on developing lightweight, high-precision localization and detection algorithms that enable harvesting robots to accurately identify and pick safflower filaments in complex, natural environments. Guo’s major contributions include the creation of the SDC-DeepLabv3+ model, which overcomes difficulties posed by small, densely clustered filaments and heavy occlusion, and an improved Faster R-CNN model incorporating split attention mechanisms to enhance detection under varying light, weather, and foliage conditions. He also pioneered a filament-necking localization method that combines an improved particle swarm optimization algorithm with a rotated rectangle approach, significantly boosting picking accuracy. Each of these three key papers has garnered 17 citations, reflecting their immediate impact on the field. Guo’s innovations directly address the critical bottleneck of precise filament localization, advancing the viability of automated safflower harvesting and contributing to the broader goal of intelligent, efficient agricultural robotics.
Research Focus
Key Achievements
Top Papers
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