Luo Haoxuan
Papers
1
Total Citations
17
H-Index
1
About
Luo Haoxuan is a rising researcher in agricultural artificial intelligence and computer vision, with a focused expertise in deep learning for precision agriculture. His most cited work, "Tea bud DG: A lightweight tea bud detection model based on dynamic detection head and adaptive loss function" (2024), has already garnered 17 citations, signaling significant early impact. In this study, Luo developed a novel, computationally efficient detection model that addresses the critical challenge of accurately identifying tea buds in complex field environments. By integrating a dynamic detection head and an adaptive loss function, his approach achieves high precision while maintaining a lightweight architecture, making it suitable for real-time deployment on resource-constrained devices like drones or edge computing systems. This contribution is pivotal for automating tea harvesting, reducing labor costs, and improving yield quality. Luo’s work exemplifies how tailored deep learning solutions can transform traditional agricultural practices, and his growing citation count reflects the community’s recognition of his practical, scalable innovations. His research stands as a model for applying AI to solve real-world agricultural problems.
Research Focus
Key Achievements
Top Papers
- 1