Papers
2
Total Citations
9
H-Index
2
About
Weihua Huang is a leading researcher in agricultural robotics and intelligent perception, specializing in lightweight deep learning models for real-time crop detection and localization. His work bridges the gap between advanced computer vision and practical edge computing, enabling autonomous agricultural systems to operate efficiently in complex field environments. Huang’s most notable contribution is the development of Slim-Banana, an efficient and low-complexity convolutional neural network for banana detection and localization, which was the first implementation of such a system on edge devices. By integrating a RealSense depth sensor with time-of-flight technology, he achieved precise 3D positioning in orchards, a breakthrough for agricultural robots. His paper on this work has garnered 6 citations, reflecting its immediate impact. More recently, Huang introduced EdgeSugarcane, a lightweight, high-precision method for real-time sugarcane node detection in edge computing environments, addressing the critical need for accurate node detection in intelligent cutting and planting. This work, with 3 citations, overcomes challenges of large model sizes and poor deployment adaptability. Huang’s research is pivotal for advancing smart agriculture, offering scalable, real-time solutions that empower robots to perform delicate tasks with unprecedented accuracy and efficiency.
Research Focus
Key Achievements
Top Papers
- 1
- 2