Limei Li

Papers

Total Citations

H-Index

About

Limei Li is a researcher at the forefront of agricultural automation and computer vision, with a primary focus on applying deep learning to precision agriculture. Her most-cited work, "Recognition Model for Tea Grading and Counting Based on the Improved YOLOv8n" (2024, 12 citations), addresses a critical bottleneck in the automation of tea-picking robots: the accurate grading and counting of tea leaves in dense, natural environments. Li’s major contribution lies in enhancing the YOLOv8n architecture to overcome challenges such as limited feature extraction and high false detection rates, enabling reliable real-time classification of tea quality. This innovation directly supports the development of intelligent harvesting systems, reducing labor dependency and improving yield assessment. While her citation count is still growing, the practical significance of her work—bridging advanced object detection with agricultural robotics—marks her as an emerging voice in the field. Her research exemplifies how tailored improvements to state-of-the-art models can solve domain-specific problems, offering a blueprint for similar applications in crop monitoring and automated sorting.