Xiaobo Cai
Papers
1
Total Citations
41
H-Index
1
About
Xiaobo Cai is a leading researcher in agricultural robotics and intelligent detection systems, with a primary focus on precision tea harvesting technologies. His most significant contribution is the development of the ShuffleNetv2-YOLOv5-Lite-E model, a lightweight deep learning architecture designed for edge device deployment. This innovation directly addresses the critical challenge of real-time, accurate detection of tea leaves with one bud and two leaves—a key requirement for automated tea-picking robots. By replacing traditional, computationally heavy feature extraction networks with an efficient alternative, Cai’s work enables high-speed, low-power recognition on portable devices, bridging the gap between advanced AI and practical field applications. His 2023 paper on this method has already garnered 41 citations, underscoring its immediate impact on the agricultural robotics community. Cai’s research stands out for its practical orientation, offering a scalable solution that enhances the precision and viability of robotic harvesting, with potential to revolutionize labor-intensive tea cultivation. His achievements mark him as a pivotal figure in the intersection of edge computing and smart agriculture.
Research Focus
Key Achievements
Top Papers
- 1