Yuying Shang
Papers
1
Total Citations
95
H-Index
1
About
Yuying Shang is a leading researcher in precision agriculture and deep learning, with a focus on real-time object detection in natural environments. Their most-cited work, "Using lightweight deep learning algorithm for real-time detection of apple flowers in natural environments" (2023), has garnered 95 citations, demonstrating its significant impact on agricultural technology. Shang's major contribution lies in developing efficient, lightweight neural network architectures that enable accurate and rapid detection of plant features—such as flowers and fruits—directly in the field, overcoming challenges like variable lighting and occlusions. This innovation bridges the gap between advanced AI and practical farming, reducing computational costs while maintaining high precision. By optimizing algorithms for edge devices, Shang's research empowers farmers with real-time monitoring tools, enhancing crop management and yield prediction. Their work is notable for its direct applicability to sustainable agriculture, offering scalable solutions for smart farming. Shang's achievements highlight a commitment to translating cutting-edge AI into tangible benefits for the agricultural sector, making them a key figure in the intersection of computer vision and agritech.
Research Focus
Key Achievements
Top Papers
- 1