Papers
2
Total Citations
8
H-Index
2
About
Minqiu Kuang is a rising researcher at the forefront of agricultural robotics and intelligent perception, with a focus on solving critical challenges in precision horticulture. Their work centers on two key areas: lightweight deep learning models for fruit detection and the integration of robotic systems for automated pollination. Kuang’s major contribution is the development of DDM-YOLO, a novel oriented detection model designed to accurately recognize mature daylily fruits in complex field environments—addressing the difficult problem of slender, densely clustered buds with diverse orientations. This work, already garnering early citations, demonstrates a practical path toward real-time, low-computation harvesting solutions. Complementing this, Kuang authored a comprehensive review on robotic pollination for greenhouse pepper breeding, synthesizing advances in target recognition, path planning, and motion control while identifying critical gaps in perception-decision integration. With both papers accumulating citations rapidly since their 2026 publication, Kuang is establishing a strong early-career impact. Their research bridges the gap between computer vision and agricultural automation, offering scalable, efficient tools that promise to enhance crop yield and labor efficiency in modern smart farming systems.
Research Focus
Key Achievements
Top Papers
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