Rongrong Wang
Papers
2
Total Citations
60
H-Index
2
About
Rongrong Wang is a leading researcher in agricultural robotics and computer vision, specializing in intelligent fruit detection and automated harvesting systems. Her work focuses on developing lightweight, efficient deep learning models that can operate reliably in complex orchard environments. Wang's most impactful contribution is the creation of Improved YOLOv5s, a novel lightweight network integrating ghost modules and coordinate attention mechanisms for all-weather dragon fruit detection, which has garnered 34 citations since 2022. She further advanced the field with YOLOMS, a multi-task CNN model that simultaneously recognizes mangoes and locates stem picking points, achieving 26 citations since 2024. These innovations address critical challenges in precision agriculture: reducing model complexity for real-time deployment on resource-constrained devices while maintaining high detection accuracy under varying lighting and occlusion conditions. Wang's research bridges the gap between theoretical computer vision and practical agricultural applications, enabling autonomous fruit detection and robotic harvesting in natural environments. Her work has significant implications for reducing labor costs and improving harvest efficiency in fruit production, establishing her as a key contributor to the growing field of smart agriculture and precision horticulture.
Research Focus
Key Achievements
Top Papers
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- 2