Yuqi Lu
Papers
2
Total Citations
17
H-Index
2
About
Yuqi Lu is an emerging researcher specializing in computer vision and precision agriculture, with a particular focus on developing advanced instance segmentation algorithms for autonomous robotic systems in horticultural environments. Lu's work addresses one of the most technically demanding challenges in agricultural automation: the accurate detection and segmentation of homochromatic fruit — specifically green fruit — against visually complex orchard backgrounds, where conventional algorithms consistently struggle. Lu's most notable contributions include Polar-Net (2022), a novel segmentation framework designed to improve picking efficiency for homo-chromatic fruit in challenging real-world orchard conditions, and Mask Positioner (2023), an algorithm engineered to generate precise fruit segmentation masks to support intelligent orchard management and harvesting robot deployment. Together, these two papers have accumulated 17 citations, reflecting growing recognition within the agricultural robotics and computer vision communities. By bridging deep learning methodologies with practical agricultural applications, Lu's research directly advances the viability of autonomous fruit-picking robots — a technology with significant implications for labor efficiency and sustainable food production. Students and researchers working at the intersection of machine learning, robotics, and smart agriculture will find Lu's contributions a valuable reference for tackling real-world visual recognition challenges in unstructured outdoor environments.
Research Focus
Key Achievements
Top Papers
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