Gookhwan Kim
Papers
3
Total Citations
8
H-Index
2
About
Gookhwan Kim is a precision agriculture engineer whose work directly addresses critical challenges in modern farming: labor efficiency, environmental sustainability, and autonomous operation. His research centers on developing intelligent sensing and control systems for agricultural robotics, with a particular focus on autonomous tractors and orchard sprayers. Kim’s most cited work, “Tillage boundary detection based on RGB imagery classification for an autonomous tractor” (2020, 5 citations), introduced a deep learning pipeline that enables a tractor to visually identify and navigate field edges without human intervention—a foundational step toward fully autonomous tillage. He further advanced smart spraying technology with his 2021 study on a LiDAR-based, high-efficiency spray control algorithm, which uses Gaussian filtering to precisely target pesticide application, drastically reducing chemical waste and farmer exposure. This work directly addresses the dual problems of environmental pollution and operator health risks in orchard management. Kim also contributed to the broader precision agriculture community by co-authoring the selected abstracts for the 10th Asian-Australasian Conference on Precision Agriculture (2024), highlighting transformative technologies in smart farming. His research is a clear example of how computer vision and sensor fusion are being deployed to make agriculture safer, cleaner, and more autonomous.
Research Focus
Key Achievements
Top Papers
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