Cheoul Young Kim
Papers
1
Total Citations
12
H-Index
1
About
Cheoul Young Kim is a leading researcher in agricultural robotics and precision horticulture, with a focus on computer vision and deep learning for plant phenotyping. His work centers on developing intelligent systems for automated crop monitoring, particularly for high-value greenhouse crops like tomatoes. Kim’s major contributions include pioneering the use of CycleGAN-based depth image conversion models to enhance the identification of growing tomato trusses—a critical task for yield prediction and environmental control. His 2022 paper on this topic, which has garnered 12 citations, demonstrates a novel approach to overcoming challenges in depth sensing under variable lighting and occluded conditions. By enabling more accurate, non-destructive detection of plant structures, his research directly supports the advancement of smart farming technologies. Kim’s work is notable for bridging the gap between synthetic and real agricultural imagery, improving the robustness of AI models in real-world settings. His contributions are essential for developing autonomous systems that can optimize resource use and boost crop productivity, making him a key figure in the future of sustainable agriculture.
Research Focus
Key Achievements
Top Papers
- 1