Xiuhong Wu
Papers
2
Total Citations
42
H-Index
2
About
Xiuhong Wu is a leading researcher in agricultural artificial intelligence, specializing in computer vision and deep learning for precision horticulture. Her work focuses on developing robust, real-time detection systems that can simultaneously identify fruits and their supporting structures—a critical challenge for automated harvesting and yield estimation. Wu’s most impactful contribution is her improved YOLOv8 model, which achieves high-accuracy, simultaneous detection of mangoes and their fruiting stems, optimized for deployment on edge devices with limited computational resources. This innovation, detailed in her 2024 paper that has already garnered 38 citations, bridges the gap between advanced deep learning and practical, on-field agricultural robotics. By enabling efficient, low-latency inference on edge hardware, Wu’s research directly supports the development of autonomous harvesting systems, reducing labor costs and post-harvest losses. Her work exemplifies the integration of state-of-the-art AI with real-world agricultural needs, positioning her as a key figure in the transition toward smart, data-driven farming.
Research Focus
Key Achievements
Top Papers
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