Papers
57
Total Citations
3,886
H-Index
34
About
Xiangjun Zou is a leading researcher in agricultural robotics and machine vision, whose work has fundamentally advanced the development of intelligent fruit-harvesting systems. Based primarily within complex, unstructured orchard environments, Zou's research bridges computer vision, deep learning, and robotic motion planning to solve real-world challenges in automated crop harvesting. His highly cited 2020 review on recognition and localization methods for vision-based fruit-picking robots — now accumulating over 546 citations — has become a cornerstone reference in the field, synthesizing machine vision's transformative role in modern precision agriculture. Beyond survey work, Zou has made substantial technical contributions across diverse fruit crops including litchi, guava, and grapes, pioneering methods for 3D fruit detection, pose estimation, and peduncle identification that enable robots to approach and harvest targets without damaging surrounding vegetation. His work on path planning, incorporating RRT algorithms and recurrent deep reinforcement learning for collision-free manipulation, demonstrates an impressive integration of perception and control. With several papers exceeding 130 citations and a cumulative impact spanning thousands of references, Zou's research portfolio positions him as an essential voice shaping the future of autonomous agricultural machinery and smart farming technologies.
Research Focus
Key Achievements
Top Papers
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- 2RRT-based path planning for an intelligent litchi-picking manipulator173 citations · 2018
- 3Color-, depth-, and shape-based 3D fruit detection172 citations · 2019
- 4Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field158 citations · 2019
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