Shanshan Shao
Papers
1
Total Citations
76
H-Index
1
About
Shanshan Shao is a leading researcher in agricultural robotics and computer vision, whose work focuses on intelligent monitoring systems for precision farming. Her most impactful contribution is the development of the YOLO-Deepsort network, a pioneering visual tracking framework that enables inspection robots to identify, track, and count tomatoes across different growth stages. This innovation, detailed in her highly cited 2022 paper (76 citations), integrates YOLOv5s with ShuffleNetV2 and a Convolutional Block Attention Module (CBAM) to achieve efficient, real-time detection. By solving the critical challenge of automated growth period monitoring and yield prediction, Shao’s work directly supports smart agriculture, reducing labor costs and improving crop management accuracy. Her research exemplifies the fusion of deep learning and robotics for practical agricultural applications, offering scalable solutions for greenhouse and field environments. With growing recognition in the field, Shao continues to advance vision-based inspection systems, positioning her as a key contributor to the future of autonomous farming technologies.
Research Focus
Key Achievements
Top Papers
- 1