Papers
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Total Citations
1
H-Index
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About
Xueqi Zhao is a rising researcher at the intersection of artificial intelligence, smart agriculture, and cyber-physical security. Their work focuses on developing novel deep learning architectures to enhance the safety and efficiency of autonomous agricultural systems. Zhao’s most notable contribution is the GDMR-Net, a graphic detection neural network that integrates multi-crossed attention mechanisms and rotation annotation—an innovative approach designed to improve object recognition in complex, unstructured farm environments. This work directly addresses critical challenges in supply chain and cyber security for IoT-enabled agricultural robots. While still early in their career, Zhao’s research has already garnered attention, with their flagship paper accumulating citations that underscore its relevance to both agronomic applications and cybersecurity. By bridging computer vision, robotics, and agricultural supply chain protection, Zhao is helping to lay the groundwork for resilient, intelligent farming systems. Their work signals a promising trajectory in a field where technological innovation is essential for global food security and infrastructure safety.
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