Ming Wen
Papers
2
Total Citations
25
H-Index
2
About
Ming Wen is a researcher at the forefront of agricultural robotics and computer vision, specializing in the development of intelligent detection systems for automated fruit harvesting. Her major contributions center on enhancing the accuracy and efficiency of robotic perception in complex agricultural environments, particularly for clustered fruits like cherry tomatoes. Wen’s most influential work, "DCFA-YOLO: A Dual-Channel Cross-Feature-Fusion Attention YOLO Network for Cherry Tomato Bunch Detection," has garnered 17 citations and introduces a novel dual-channel architecture that fuses color and depth information to significantly improve detection performance. Building on this, her paper "Cherry Tomato Bunch and Picking Point Detection for Robotic Harvesting Using an RGB-D Sensor and a StarBL-YOLO Network" (8 citations) addresses the critical challenge of identifying precise picking points on bunches, a key bottleneck for practical robotic deployment. Wen’s innovative use of multimodal sensor data and attention mechanisms is paving the way for more reliable and autonomous harvesting systems, directly impacting the efficiency of modern agriculture. Her work is essential reading for students and researchers interested in the intersection of deep learning, robotics, and precision agriculture.
Research Focus
Key Achievements
Top Papers
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