Papers
7
Total Citations
196
H-Index
5
About
Wenwu Lu is a leading researcher in agricultural robotics and intelligent perception, whose work is transforming how autonomous systems interact with complex, unstructured farm environments. His primary research areas include computer vision for fruit detection, 3D pose estimation, and safe trajectory planning for agricultural vehicles. Lu’s most influential contribution is the development of DSW-YOLO, a deep learning detection method tailored for ground-planted strawberry fruits under various occlusion levels, which has garnered over 107 citations and set a new benchmark for precision agriculture. He has also pioneered a multitask convolutional neural network for comprehensive visual information acquisition in tomato-picking robots, and a keypoint-based 3D pose detection algorithm that enables accurate fruit localization from point cloud data. His recent work on efficient and safe headland turning for autonomous agricultural vehicles in cluttered orchards addresses a critical bottleneck in field robotics. Additionally, Lu has designed a novel multistage synchronous telescopic manipulator with an end-effector–biased rotating-pulling mode, enabling damage-free robotic picking. With over 190 total citations and a portfolio of high-impact publications, Lu is shaping the future of intelligent, autonomous farming systems.
Research Focus
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Top Papers
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