Qilin An
Papers
2
Total Citations
88
H-Index
2
About
Qilin An is a leading researcher in agricultural robotics and computer vision, with a primary focus on developing lightweight, real-time detection models for automated fruit harvesting and orchard management. Their work addresses critical challenges in deploying AI on low-cost, GPU-free industrial computers, enabling rapid and accurate identification of crop growth stages and maturity. An’s most cited paper, “Real-Time Monitoring Method of Strawberry Fruit Growth State Based on YOLO Improved Model” (2022, 77 citations), introduces an enhanced YOLO architecture that significantly improves the speed and precision of strawberry ripeness detection, a key bottleneck for automated pollination, fertilization, and picking. Building on this, An proposed TDPPL-Net (2023, 11 citations), a lightweight model for simultaneous tomato detection and picking point localization, drastically reducing network parameters without sacrificing accuracy. These contributions have direct implications for reducing labor costs and increasing efficiency in precision agriculture. An’s work is widely recognized for bridging the gap between advanced deep learning and practical, real-world deployment in harvesting robots, making them a pivotal figure in the field of agricultural automation.
Research Focus
Key Achievements
Top Papers
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