Huiling Wei
Papers
1
Total Citations
22
H-Index
1
About
Huiling Wei is a researcher specializing in deep learning, computer vision, and agricultural robotics, with a particular focus on applying intelligent recognition systems to precision agriculture. Her most notable contribution centers on developing an improved lightweight neural network for grape detection in challenging, unstructured environments — a significant advancement for autonomous agricultural machinery. By refining the YOLOX-Tiny model, Wei addressed critical real-world obstacles such as dense fruit clustering and occlusion, problems that had long hindered the reliable performance of grape-picking robots. This work demonstrates her ability to bridge theoretical deep learning innovations with practical agricultural automation challenges, making intelligent harvesting systems more viable for real-world deployment. Her 2023 paper has already garnered 22 citations, reflecting the growing interest in her contributions within the agricultural AI and robotics communities. Wei's research sits at an important intersection of machine learning efficiency and robotic perception, areas of increasing relevance as the agricultural sector seeks scalable, automated solutions. Her work positions her as an emerging voice in smart farming technology, with implications for improving crop yield efficiency and reducing labor dependency in viticulture and beyond.
Research Focus
Key Achievements
Top Papers
- 1