Papers
1
Total Citations
3
H-Index
1
About
P. Huo is a researcher at the forefront of agricultural automation and intelligent harvesting systems, with a primary focus on computer vision and edge computing for precision agriculture. Their most notable contribution is the development of an improved YOLOv8-based detection system for sugarcane stalk nodes, a critical upstream task for robotic harvesting. Huo constructed the Sugarcane Stalk Node Dataset (SSND) to overcome challenges such as occlusion, variable lighting, and indistinct morphological features in field conditions. This work, published in 2025 and already garnering 3 citations, demonstrates Huo’s ability to bridge deep learning with real-world deployment on edge devices, enabling efficient, low-latency detection in resource-constrained environments. By addressing the gap between laboratory models and practical agricultural applications, Huo’s research holds significant promise for reducing labor costs and increasing harvest efficiency. Their work represents a key step toward fully autonomous sugarcane harvesting, with potential spillover effects for other crop detection tasks. Huo’s contributions are particularly valuable for researchers and engineers developing smart farming technologies that require robust, deployable vision systems.
Research Focus
Key Achievements
Top Papers
- 1