Shuangchen Chen

Henan University of Science and Technology

Papers

Total Citations

H-Index

About

Dr. Shuangchen Chen is a leading researcher in agricultural automation and precision computer vision, with a focus on intelligent harvesting systems. His most influential work centers on developing lightweight, high-accuracy detection models for specialty crops, particularly tea buds. In his landmark 2024 study, Chen introduced an improved YOLOv5 architecture that dramatically enhances real-time tea bud identification while reducing computational overhead—a critical advancement for deploying AI on edge devices in field conditions. This work, already garnering 7 citations, demonstrates his ability to bridge deep learning efficiency with practical agricultural needs. By optimizing model size without sacrificing detection precision, Chen’s contributions directly address the bottleneck of automated plucking: balancing speed, accuracy, and hardware constraints. His research not only advances smart agriculture but also provides a scalable framework for other fine-grained object detection tasks in horticulture. Chen’s innovations are paving the way for fully autonomous harvesting, reducing labor dependency and improving yield quality. For students and researchers, his work exemplifies how targeted algorithmic refinement can solve real-world engineering challenges in agri-tech.