Papers
12
Total Citations
309
H-Index
9
About
Lixue Zhu is a pioneering researcher in agricultural robotics and intelligent automation, whose work sits at the intersection of deep learning, computer vision, and robotic systems for precision agriculture. With a focus on fruit harvesting automation, Zhu has made significant contributions to autonomous robot navigation, target detection, and motion planning in complex, unstructured farm environments. Their most cited work — a 2021 study on collision-free path planning for a guava-harvesting robot using recurrent deep reinforcement learning (151 citations) — established Zhu as a leading voice in applying advanced AI techniques to robotic harvesting challenges. Beyond path planning, Zhu has developed innovative solutions for detecting and localizing crops such as tea buds and banana stalks using state-of-the-art architectures including YOLOv5 and YOLOX-S, and pioneered end-to-end learning systems for orchard row navigation. Their comprehensive work on a fully autonomous banana-picking robot — spanning inverse kinematics, headland turning control, and field integration — demonstrates a rare ability to bridge theoretical AI research with real-world agricultural deployment. Across ten highly cited publications, Zhu's research is shaping the future of smart farming and labor-saving robotic technology.
Research Focus
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