Vsevolod Cherepashkin
Papers
1
Total Citations
2
H-Index
1
About
Vsevolod Cherepashkin’s research sits at the intersection of computer vision, deep learning, and agricultural science, with a primary focus on 3D reconstruction for plant phenotyping. His most notable contribution is the development of a deep learning-based method for reconstructing three-dimensional models of wheat seeds from two-dimensional images, a critical advance for non-destructive, high-throughput phenotyping. This work, presented alongside a novel dataset and benchmark challenge, enables researchers to accurately measure seed shape and volume—traits directly linked to early plant development and crop yield. While his citation count is still growing, the foundational nature of this dataset and baseline method positions it as a valuable resource for the plant science and computer vision communities. By bridging the gap between advanced neural network architectures and practical agricultural needs, Cherepashkin is contributing to the future of precision breeding and food security, demonstrating how deep learning can transform traditional phenotyping into a data-rich, automated science.
Research Focus
Key Achievements
Top Papers
- 1