Jingwen Yang
Papers
1
Total Citations
5
H-Index
1
About
Jingwen Yang is a leading researcher in agricultural robotics and computer vision, with a focus on developing intelligent systems for precision harvesting in unstructured environments. Her key research areas include deep learning-based object detection, RGB-D information fusion, and robotic perception for specialty crops. Yang’s most notable contribution is her pioneering work on vision-based localization for tea-harvesting robots, where she proposed the T-YOLOv8n model—an improved deep learning architecture that significantly enhances the accuracy of detecting and segmenting tender tea shoots in complex field conditions. Her 2024 paper on this method has already garnered 5 citations, reflecting its immediate impact on the emerging field of automated tea picking. By integrating RGB and depth data, Yang’s approach solves the critical challenge of precisely identifying picking points in cluttered, variable-lighting environments, directly enabling more efficient and damage-free harvesting. Her work bridges the gap between advanced computer vision algorithms and practical agricultural automation, offering a scalable solution for labor-intensive crop harvesting. Yang’s research is essential reading for anyone interested in robotic perception, precision agriculture, or the application of YOLO-based models to real-world field robotics.
Research Focus
Key Achievements
Top Papers
- 1