Boyu Ying

Papers

Total Citations

H-Index

About

Boyu Ying is a researcher at the forefront of precision agriculture, specializing in computer vision and deep learning for intelligent weed management. His most cited work, "Weed Detection in Images of Carrot Fields Based on Improved YOLO v4" (2021, 62 citations), addresses a critical challenge in sustainable farming: the accurate, real-time identification of weeds among crops. By refining the YOLO v4 architecture into a lightweight model, Ying significantly enhanced detection speed and accuracy for diverse weed species in carrot fields, enabling more efficient and environmentally friendly herbicide application. This contribution has been widely recognized, with his paper serving as a key reference for researchers developing automated weeding systems. Ying’s work exemplifies the practical application of AI to agricultural robotics, bridging the gap between advanced neural networks and real-world field deployment. His research not only advances precision prevention and control of weeds but also reduces labor and chemical inputs, making farming more sustainable. For students and researchers in agricultural technology, Ying’s innovations offer a compelling model of how deep learning can transform traditional practices into data-driven, automated solutions.