Xiongwei He
Papers
1
Total Citations
4
H-Index
1
About
Xiongwei He is a researcher at the forefront of agricultural robotics and precision detection, with a particular focus on intelligent harvesting technologies. His work centers on developing advanced computer vision models to solve real-world agricultural challenges, notably through his highly cited paper "YOLOv7-Branch: A Jujube Leaf Branch Detection Model for Agricultural Robot" (2024, 4 citations). This study addresses a critical bottleneck in jujube leaf tea production—the precise identification of leaf branches—which directly impacts both yield and quality. By adapting the YOLOv7 architecture for agricultural contexts, He demonstrates how deep learning can enable robots to accurately detect and harvest specific plant structures, a breakthrough that promises to automate labor-intensive tasks and boost crop output. His research bridges the gap between state-of-the-art AI and practical farming needs, offering scalable solutions for precision agriculture. With this work, He contributes to a growing body of knowledge that could transform how robots interact with crops, making intelligent harvesting more efficient and accessible for farmers worldwide.
Research Focus
Key Achievements
Top Papers
- 1