Huaiqu Feng
Papers
2
Total Citations
218
H-Index
2
About
Huaiqu Feng is a leading researcher in agricultural robotics and precision farming, with a primary focus on computer vision and deep learning for autonomous crop management. His work addresses critical challenges in field-based perception, particularly for early-stage crop detection and navigation. Feng’s most influential contribution is the development of an improved Faster R–CNN architecture for maize seedling detection, published in 2019, which has garnered 189 citations. This method robustly identifies seedlings across varying growth stages and complex field conditions—such as occlusion, lighting changes, and soil variability—significantly advancing the reliability of automated weeding and monitoring systems. He further pioneered the row anchor selection classification method for early-stage crop row-following (2021), enabling precise autonomous navigation in unstructured agricultural environments. By integrating deep learning with practical field constraints, Feng’s work bridges the gap between laboratory models and real-world deployment. His research is widely cited by engineers and agronomists developing intelligent farming equipment, and his methods serve as foundational benchmarks for vision-guided agricultural robots.
Research Focus
Key Achievements
Top Papers
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