Airu Zou

Papers

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About

Airu Zou has made impactful contributions at the intersection of agricultural robotics and computer vision, with a particular focus on lightweight, real-time detection algorithms for fruit harvesting. Her most cited work, "Lightweight Detection Algorithm of Kiwifruit Based on Improved YOLOX-S" (2022, 35 citations), addresses a critical challenge in precision agriculture: enabling mobile picking robots to accurately identify small, occluded kiwifruit targets under computational constraints. By enhancing the YOLOX-S architecture, Zou’s research improves feature extraction for small-scale aggregation, achieving a balance between detection speed and accuracy that is essential for field deployment. This work has been widely referenced by researchers developing edge-computing solutions for agricultural automation. Zou’s research areas include deep learning, object detection, and robotic perception, with her contributions directly supporting the advancement of smart farming technologies. Her ability to adapt state-of-the-art models for resource-limited platforms positions her as a key figure in making AI-driven harvesting practical and efficient.