Taek Sung Lee
Papers
1
Total Citations
12
H-Index
1
About
Taek Sung Lee is a leading researcher in agricultural robotics and precision phenotyping, with a focus on developing advanced computer vision and deep learning techniques for plant monitoring. His work centers on the automated identification and analysis of tomato plant structures, particularly the growing truss—the critical stem segment where flowers and fruit develop. Lee’s major contribution includes the creation of a depth image conversion model based on CycleGAN, which enhances the accuracy of growing truss detection under varying environmental conditions. This innovation, detailed in his 2022 paper with 12 citations, addresses a key challenge in non-invasive plant sensing, enabling real-time growth monitoring and optimized greenhouse management. By leveraging generative adversarial networks for image translation, Lee has advanced the integration of AI in agriculture, improving yield prediction and resource efficiency. His research bridges the gap between computer vision and crop science, offering scalable solutions for smart farming. With a growing citation impact, Lee’s work is foundational for students and researchers exploring automated plant phenotyping and sustainable agricultural technologies.
Research Focus
Key Achievements
Top Papers
- 1