Hyun-Jong Kim
Papers
2
Total Citations
7
H-Index
2
About
Hyun-Jong Kim’s research focuses on agricultural robotics and autonomous navigation, with key contributions in deep learning-based perception for field operations and wireless sensor network integration for mobile robot localization. His most cited work, “Tillage boundary detection based on RGB imagery classification for an autonomous tractor” (2020, 5 citations), presents a novel deep learning pipeline that combines image cropping, object classification, area segmentation, and boundary detection to enable autonomous tillage using only a standard RGB camera. This approach addresses a critical challenge in precision agriculture—robust field boundary perception under varying conditions—and lays groundwork for cost-effective autonomous farming systems. Earlier, Kim explored indoor mobile robot localization using ZigBee-based wireless sensor networks and fuzzy modeling (2008, 2 citations), demonstrating how low-power, low-cost sensor networks can estimate robot position via RSSI values. This work contributed to the broader field of indoor navigation for service robots. Kim’s research bridges practical agricultural automation with foundational sensor fusion techniques, offering scalable solutions for autonomous machinery. His work is particularly relevant for researchers developing vision-based navigation systems for unstructured outdoor environments and those seeking to integrate wireless sensor networks with mobile robotics.
Research Focus
Key Achievements
Top Papers
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- 2