Yulong Nan
Papers
3
Total Citations
115
H-Index
2
About
Dr. Yulong Nan is a leading researcher in agricultural robotics and computer vision, specializing in intelligent fruit detection for automated harvesting systems. His work focuses on developing lightweight, high-speed deep learning models that enable robots to accurately identify and pick fruits in complex field environments. Dr. Nan’s major contributions include the WGB-YOLO network for multi-class pitaya detection (83 citations), which significantly improves precision in densely planted orchards, and a NSGA-II-based pruned YOLOv5l for faster green pepper detection (31 citations), demonstrating a novel approach to model optimization. His latest research on an improved lightweight Faster R-CNN based on MobileNetV3 tackles the critical challenge of balancing model size and detection speed for real-time robotic picking. With a total of over 115 citations across his most-cited works, Dr. Nan’s innovations are directly advancing the practicality of precision agriculture, making autonomous fruit harvesting more efficient and accessible. His work is essential reading for researchers and students in agricultural engineering, computer vision, and robotics.
Research Focus
Key Achievements
Top Papers
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