Hyongsuk Kim
Papers
9
Total Citations
141
H-Index
4
About
Hyongsuk Kim is a leading researcher in precision agriculture robotics, specializing in deep learning for autonomous farming systems. His work focuses on developing intelligent vision-based solutions for crop management, including semantic segmentation for weed detection, fruit identification, and yield prediction. Kim's most influential contribution is his 2019 paper on learning semantic graphics using convolutional encoder-decoder networks for autonomous weeding in paddy fields, which has garnered 68 citations and addresses the critical challenge of reducing chemical herbicide use. He has further advanced agricultural robotics with innovative work on strawberry segmentation using hierarchical adaptive feature selection (23 citations) and intelligence-guided visual servoing for watermelon pollination (20 citations). His research extends to practical applications such as strawberry yield prediction via semantic graphics (15 citations) and disease detection in paprika using YOLOv4 models. Beyond agriculture, Kim has contributed to autonomous robot navigation, including golf ball collection robots and FPGA-based manipulator control. His work bridges computer vision, deep learning, and robotics to create sustainable, efficient farming technologies that reduce environmental impact while improving crop productivity.
Research Focus
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