Lijiao Chen

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About

Lijiao Chen is a researcher whose work sits at the intersection of computer vision and agricultural automation, with a particular focus on intelligent tea harvesting. Her primary research areas include deep learning-based object detection, precision agriculture, and the development of automated grading systems for specialty crops. Chen’s most notable contribution is her work on the "Recognition Model for Tea Grading and Counting Based on the Improved YOLOv8n," which addresses the critical challenge of accurately identifying and grading tea leaves in complex, natural environments. This study tackles the real-world problems of dense leaf distribution, limited feature extraction, and high false-detection rates—issues that are fundamental to enabling the next generation of autonomous tea-picking robots. By refining the YOLOv8n architecture, Chen has provided a practical, high-efficiency solution that bridges the gap between advanced AI models and field-deployable agricultural technology. With 12 citations, this work is already gaining traction among researchers in precision agriculture and robotics. Chen’s research is paving the way for more intelligent, automated harvesting systems, directly contributing to the future of smart farming and sustainable crop management.