Hongqi Wu
Papers
1
Total Citations
29
H-Index
1
About
Hongqi Wu is a researcher at the forefront of precision agriculture and computer vision, specializing in deep learning for real-time weed detection. His most-cited work, "YOLOv8-ECFS: A lightweight model for weed species detection in soybean fields" (2024, 29 citations), introduces an innovative adaptation of the YOLOv8 architecture—enhanced with efficient channel feature selection—to achieve high-accuracy, low-latency identification of weed species in complex field environments. This contribution addresses a critical bottleneck in sustainable farming: enabling autonomous, site-specific herbicide application while minimizing computational cost. Wu’s research bridges the gap between state-of-the-art object detection and practical agricultural deployment, demonstrating how lightweight neural networks can operate effectively on edge devices. With a growing citation footprint and a focus on real-world impact, his work is shaping the next generation of smart farming tools, offering scalable solutions for crop protection and yield optimization.
Research Focus
Key Achievements
Top Papers
- 1