Xueyan Zhu
Papers
2
Total Citations
89
H-Index
2
About
Xueyan Zhu is a leading researcher in agricultural artificial intelligence, specializing in deep learning-based fruit maturity detection for orchard environments. Her work focuses on developing lightweight, efficient computer vision models that enable real-time, in-field assessment of fruit ripeness, directly addressing the challenges of automated harvesting and precision agriculture. Zhu’s major contributions include the creation of modified YOLO architectures for detecting Camellia oleifera fruit maturity, a system that achieved 55 citations for its practical balance of accuracy and computational efficiency. She further advanced the field with Olive-EfficientDet, a multi-cultivar olive fruit maturity detection framework (34 citations), demonstrating robust performance across diverse orchard conditions. Her research is notable for bridging the gap between high-accuracy deep learning and the resource constraints of agricultural robotics, offering scalable solutions for non-destructive, automated quality assessment. By integrating lightweight neural networks with real-world deployment needs, Zhu’s work has significantly impacted the development of intelligent agricultural systems, providing a foundation for future innovations in fruit monitoring and harvest optimization.
Research Focus
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Top Papers
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