Tianxiang Yan
Papers
2
Total Citations
13
H-Index
2
About
Tianxiang Yan is a rising researcher at the intersection of agricultural robotics and intelligent automation, whose work addresses critical challenges in precision agriculture and autonomous navigation. His primary research areas include deep learning-based object detection, robotic path planning, and computer vision for agricultural applications. Yan’s most notable contribution is the development of YOLO-CT, an innovative method that enhances the YOLOv8n-Pose architecture to simultaneously detect multi-species mature cherry tomatoes and locate optimal picking points in complex, unstructured environments—a breakthrough that directly addresses the bottleneck in automated fruit harvesting. This work has already garnered 9 citations since its 2025 publication, signaling its immediate impact on the field. Additionally, Yan has advanced mobile robotics through a convolutional neural network-based deep Q-network (CNN-DQN) path planning method, enabling more intelligent and adaptive navigation in dynamic settings. By bridging state-of-the-art computer vision with reinforcement learning, Yan’s research offers practical solutions for real-world agricultural automation, making him a promising voice in the next generation of robotics and AI researchers.
Research Focus
Key Achievements
Top Papers
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- 2