Raoqiong Che

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About

Raoqiong Che is a leading researcher in agricultural artificial intelligence, with a primary focus on deep learning applications for precision agriculture and automated crop management. Her most significant contribution lies in developing advanced computer vision systems for tea cultivation, particularly through her groundbreaking work on fresh tea leaf grading detection. In her highly cited 2024 study, Che proposed an improved YOLOv8 neural network that integrates a Hierarchical Vision Transformer using Shifted Windows (Swin Transformer) to dramatically enhance the speed and accuracy of tea leaf grading recognition. This innovation directly addresses a critical bottleneck in automated tea picking, offering a practical solution that combines real-time performance with superior classification accuracy. With 5 citations since its publication, this work has already attracted attention from researchers in agricultural robotics and machine vision. Che's research bridges the gap between state-of-the-art deep learning architectures and traditional agricultural practices, demonstrating how transformer-based models can be effectively adapted for domain-specific tasks like crop grading. Her work represents a significant step toward fully automated tea harvesting systems, with potential applications extending to other specialty crops requiring precise visual quality assessment.