Shuaiying Yu

Jiangxi University of Science and Technology

Papers

Total Citations

311

H-Index

About

Shuaiying Yu is a leading researcher in agricultural automation and computer vision, with a focus on developing lightweight, high-precision detection algorithms for smart farming. Yu’s most influential work, a 2023 study on a lightweight YOLOv8 tomato detection algorithm combining feature enhancement and attention mechanisms, has garnered over 311 citations, underscoring its significance in the field. This research directly addresses the critical challenge of low automation in tomato harvesting by proposing an improved YOLOv8s model that balances accuracy and computational efficiency. The method provides essential technical support for automatic harvesting and classification, enabling real-time fruit detection in complex agricultural environments. Beyond this flagship contribution, Yu’s work consistently advances the integration of deep learning with agricultural robotics, aiming to reduce labor dependency and increase yield efficiency. By prioritizing lightweight architectures—ideal for edge devices in field settings—Yu’s innovations have practical implications for the future of precision agriculture. Their research is widely cited by peers working on object detection, feature enhancement, and attention mechanisms in agricultural applications, marking Yu as a key figure in the movement toward fully automated crop management systems.