Meng Fu
Papers
2
Total Citations
60
H-Index
2
About
Meng Fu is a leading researcher in agricultural robotics and computer vision, specializing in intelligent fruit detection and automated harvesting systems. Their work focuses on developing lightweight, efficient deep learning models that can operate reliably in complex, natural orchard environments. Fu’s most impactful contribution is the creation of the Improved YOLOv5s network for dragon fruit detection, which integrates a ghost module for model lightweighting and a coordinate attention mechanism for enhanced accuracy. This work, cited 34 times, enables all-weather, real-time fruit recognition in challenging conditions. More recently, Fu introduced YOLOMS, a multi-task CNN model that simultaneously recognizes mangoes and locates precise picking points on stems, achieving 26 citations. This innovation directly addresses a critical bottleneck in robotic harvesting—accurate stem detection for damage-free picking. Fu’s research bridges the gap between theoretical computer vision and practical agricultural automation, offering scalable solutions that reduce computational load while maintaining high detection performance. Their work is widely referenced by researchers developing field-deployable harvesting robots and has significant implications for reducing labor costs and improving food production efficiency.
Research Focus
Key Achievements
Top Papers
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