Xiujuan Deng
Papers
1
Total Citations
41
H-Index
1
About
Xiujuan Deng is a leading researcher in agricultural robotics and deep learning, with a primary focus on intelligent tea harvesting systems. Her most influential work tackles the critical challenge of real-time, accurate detection of tea shoots with one bud and two leaves—the optimal standard for premium tea production. In her highly cited 2023 paper, Deng proposed an edge device detection method based on ShuffleNetv2-YOLOv5-Lite-E, which replaces the original feature extraction network to enable lightweight, efficient recognition directly on picking robots. This contribution has garnered 41 citations, reflecting its practical significance for automating tea cultivation. Deng’s research bridges computer vision and agricultural engineering, demonstrating how optimized neural networks can operate on resource-constrained devices without sacrificing accuracy. Her work not only advances precision agriculture but also provides a scalable solution for labor-intensive tea harvesting, positioning her as a key innovator in smart farming technologies.
Research Focus
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Top Papers
- 1