Jingying Cui

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About

Jingying Cui is a leading researcher in agricultural automation and intelligent detection systems, with a primary focus on computer vision and deep learning applications for precision agriculture. Her most notable work centers on developing lightweight, high-efficiency object detection models for tea bud identification, addressing critical challenges in automated tea plucking. In her highly cited 2024 study, Cui proposed an improved YOLOv5-based method that significantly enhances picking accuracy while reducing computational demands, achieving a balance between real-time performance and model portability for field deployment. With 7 citations already, this work demonstrates immediate impact in the agricultural robotics community. Cui’s contributions are pivotal for advancing smart agriculture, enabling cost-effective, labor-saving solutions for tea cultivation. Her research not only improves operational efficiency but also supports sustainable farming practices by minimizing resource waste. By integrating state-of-the-art neural network architectures with domain-specific agricultural needs, Jingying Cui is helping to bridge the gap between cutting-edge AI and practical, real-world farming applications.