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About

Shaoming Luo is a leading researcher in agricultural robotics and computer vision, specializing in the development of intelligent harvesting systems for complex natural environments. His work focuses on enabling autonomous fruit picking through advanced perception techniques, including object detection, semantic segmentation, and 3D localization. Luo’s major contributions include pioneering computer vision-based localisation for litchi harvesting (69 citations), the DualSeg framework that fuses transformer and CNN architectures for image segmentation in vineyards (58 citations), and improved YOLOv5s models for citrus fruit detection under variable illumination and occlusion (40 citations). His recent innovations extend to visual knowledge distillation for grape cluster picking point cognition and deep reinforcement learning (DRL)-enhanced 3D detection of occluded stems for robotic grape harvesting. With a cumulative citation count exceeding 190, Luo’s work has significantly advanced the feasibility of automated harvesting in unstructured agricultural settings. His research is widely recognized for bridging the gap between theoretical computer vision and practical field deployment, making him a key figure in the next generation of precision agriculture.