Jiqing Chen
Papers
19
Total Citations
537
H-Index
11
About
Jiqing Chen is a prominent researcher at the intersection of agricultural robotics, computer vision, and deep learning, with a focused mission to advance the automation of greenhouse harvesting and smart farming systems. Their work centers on enabling robotic systems to accurately perceive, navigate, and interact within complex agricultural environments. Chen's most influential contributions include pioneering navigation path extraction methods for greenhouse robots, notably through the prediction-point and median-point Hough transform approaches (combined 172 citations), which have become foundational references for autonomous agricultural vehicle guidance. Their prolific work in fruit detection has produced improved YOLO-based architectures for detecting tomatoes, grapes, and mangoes under challenging real-world conditions such as occlusion, dense clustering, and variable illumination—collectively accumulating over 270 citations across multiple studies. Chen has also advanced lightweight and efficient model design, demonstrating particular skill in balancing detection accuracy with computational feasibility for edge-device deployment. Their path-planning research, combining A* algorithms with artificial potential fields, further reflects a systems-level approach to agricultural robotics. With a growing citation record exceeding 490 across ten key publications, Jiqing Chen stands as a significant contributor to the future of intelligent, robot-assisted precision agriculture.
Research Focus
Key Achievements
Top Papers
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- 4An improved Yolov3 based on dual path network for cherry tomatoes detection51 citations · 2021
- 5Detecting ripe fruits under natural occlusion and illumination conditions48 citations · 2021
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