Kaifeng Yang
Papers
1
Total Citations
9
H-Index
1
About
Kaifeng Yang is a researcher whose work sits at the intersection of precision agriculture and deep learning, with a particular focus on weed and crop recognition. His most cited paper, "Beet seedling and weed recognition based on convolutional neural network and multi-modality images" (2021, 9 citations), exemplifies his core contribution: developing robust, multi-modal imaging approaches that leverage convolutional neural networks to distinguish between crops and weeds in complex field environments. This work addresses a critical bottleneck in automated weeding systems, offering a pathway toward more efficient, chemical-free farming. Yang’s research is notable for its practical integration of different image types—such as RGB and near-infrared—to improve classification accuracy under real-world conditions. While his citation count reflects an early-career stage, the applied nature of his studies holds significant promise for advancing smart agriculture technologies. By bridging computer vision with agronomic needs, Yang is helping to lay the groundwork for autonomous, data-driven crop management systems that could reduce labor and environmental impact in the years to come.
Research Focus
Key Achievements
Top Papers
- 1