Papers
7
Total Citations
141
H-Index
6
About
Zhengwei Guo is a leading researcher in agricultural robotics and intelligent automation, with a specialization in robotic harvesting systems for fruit crops such as apples and tomatoes. His work sits at the intersection of computer vision, deep learning, and robotic systems engineering — fields he has woven together to address one of modern agriculture's most pressing challenges: the global decline in agricultural labor. Guo's most significant contributions include the development of multi-arm harvesting robot systems and advanced task planning algorithms. His pioneering use of deep reinforcement learning — notably LSTM-PPO frameworks — for dynamic multi-arm coordination (40 and 16 citations respectively) has pushed the boundaries of autonomous harvesting in unstructured orchard environments. Equally impactful is his work on apple detection, where he has engineered custom YOLO-based models — including MSOAR-YOLOv10 and SGW-YOLOv8n — capable of handling occlusion and variable lighting conditions with high accuracy (28 and 17 citations). With a publication record accumulating over 140 citations across just a handful of recent papers, Guo's research is rapidly gaining recognition. His dual-arm harvesting robot system (30 citations) exemplifies his ability to translate theoretical advances into practical, deployable technology, making him an influential voice in the future of precision agriculture and agri-robotics.
Research Focus
Key Achievements
Top Papers
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- 2Design of and Experiment with a Dual-Arm Apple Harvesting Robot System30 citations · 2024
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