Zhengyang Yi

Jiangsu University of Science and Technology

Papers

Total Citations

H-Index

About

Zhengyang Yi is a researcher at the forefront of computer vision and embedded artificial intelligence, with a specialized focus on lightweight neural networks for real-time environmental sensing. His most influential work, "Tree Detection Algorithm Based on Embedded YOLO Lightweight Network" (2022), addresses the critical challenge of deploying advanced object detection on resource-constrained devices. By optimizing the YOLO architecture for embedded systems, Yi’s algorithm enables efficient, accurate tree detection in natural environments—a breakthrough with direct applications in precision agriculture, autonomous forestry monitoring, and ecological survey automation. While his citation count of 3 reflects the emerging nature of this niche field, the work’s practical significance lies in bridging the gap between high-performance deep learning and low-power hardware. Yi’s contributions exemplify the growing demand for edge-computing solutions in environmental AI, offering a scalable framework that balances detection speed with minimal computational cost. For students and researchers exploring embedded vision systems, Yi’s approach provides a compelling case study in model compression and domain-specific optimization, demonstrating how tailored algorithms can unlock new possibilities for real-world deployment in remote or power-limited settings.