Qinghui Lai

Kunming University of Science and Technology, Yunnan Normal University

Papers

Total Citations

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About

Qinghui Lai is a leading researcher in agricultural robotics and computer vision, specializing in real-time object detection and autonomous navigation for specialty crops. His work addresses critical challenges in automated harvesting and field robotics, particularly for high-value crops like Yunnan Xiaomila peppers and Panax notoginseng. Lai’s major contributions include developing lightweight deep learning architectures that dramatically reduce computational costs while improving detection accuracy in dense, occluded environments—a key bottleneck for harvesting robots. His most cited work, "Rapid detection of Yunnan Xiaomila based on lightweight YOLOv7 algorithm" (18 citations), demonstrates a novel approach to real-time fruit detection that balances speed and precision. In his second most-cited paper (5 citations), Lai tackles the complex problem of navigation path extraction in shade houses, where traditional color-based methods fail due to similar soil and crop row appearances. His Im-YOLOv5s framework enables reliable autonomous navigation in these challenging conditions. Lai’s research directly impacts precision agriculture by making robotic harvesting and field operations more efficient and accessible, with potential applications extending to other dense, occluded crop environments.