Papers
2
Total Citations
12
H-Index
2
About
Guowei Dai is a researcher at the forefront of precision agriculture and smart greenhouse technology, specializing in the integration of computer vision and deep learning for automated crop management. His work addresses critical challenges in Agriculture 4.0, where real-time perception systems are essential for tasks like automated spraying and yield estimation. Dai’s most notable contributions include developing a lightweight vision transformer for intelligent vineyard blade density measurement, a method that enables precise, real-time differentiation of plant leaf density to optimize spraying operations. This work has garnered 8 citations since 2024. He further advanced the field with a YOLOv8-based model incorporating high- and low-frequency feature transformer structures for tomato detection and counting in smart greenhouses, achieving a remarkable correlation of 0.9282 between predicted and actual counts—demonstrating its viability as a replacement for manual counting. This innovation, with 4 citations, offers critical insights for robotic harvesting and yield estimation. Dai’s research is distinguished by its focus on lightweight, efficient models that balance accuracy with computational practicality, making his work highly relevant for real-world agricultural applications. His achievements underscore a commitment to bridging the gap between advanced AI and sustainable farming practices.
Research Focus
Key Achievements
Top Papers
- 1
- 2