Papers
2
Total Citations
7
H-Index
2
About
Dr. Maozhen Qu is a rising researcher at the intersection of agricultural robotics, computer vision, and non-destructive food quality assessment. Their work focuses on enabling real-time, automated processing of aquatic and horticultural products through advanced deep learning and edge computing. Dr. Qu’s most-cited study, “Real-time tilapia fillet defect segmentation on edge device for robotic trimming” (2024, 5 citations), pioneers the deployment of lightweight neural networks on resource-constrained hardware, directly addressing a critical bottleneck in industrial robotic trimming. This contribution demonstrates a practical pathway from lab-grade accuracy to field-ready speed. In their subsequent review, “Toward robust in-field fruit quality evaluation” (2025, 2 citations), Dr. Qu critically synthesizes emerging nondestructive technologies—such as hyperspectral imaging and portable NIR sensors—highlighting their potential and limitations for real-world grading. By bridging the gap between algorithmic innovation and deployable hardware, Dr. Qu’s work is laying the groundwork for more efficient, automated food processing systems, with clear implications for reducing waste and improving yield in aquaculture and horticulture.
Research Focus
Key Achievements
Top Papers
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