Yuhan Tan
Papers
1
Total Citations
4
H-Index
1
About
Yuhan Tan is a researcher focused on the intersection of agricultural engineering and artificial intelligence, with a particular emphasis on computer vision and deep learning applications for precision agriculture. Their most notable contribution is the development of a lightweight convolutional neural network (CNN) for detecting *Camellia oleifera* fruit in natural environments, a breakthrough that addresses the critical challenge of synchronized flowering and fruiting in these trees. This algorithm enables rapid, high-accuracy detection essential for automating harvesting—a task currently reliant on inefficient manual labor or mechanized methods that risk bud damage. While this key paper has garnered 4 citations, its practical implications for reducing agricultural labor burdens and improving crop yield efficiency are significant. Tan’s work exemplifies how tailored AI solutions can solve domain-specific problems in agriculture, paving the way for smarter, non-destructive harvesting technologies. Their research holds promise for advancing sustainable farming practices and integrating real-time machine vision into field operations.
Research Focus
Key Achievements
Top Papers
- 1