Papers
3
Total Citations
47
H-Index
3
About
Ki-Beom Lee is a researcher at the forefront of agricultural automation, specializing in the integration of hyperspectral imaging, machine learning, and robotics for precision farming. His work primarily focuses on non-destructive quality assessment and automated harvesting systems for high-value crops. Lee’s most impactful contributions include the development of snapshot-type hyperspectral imagery combined with support vector classifiers to accurately classify tomato maturity, achieving 22 citations for this foundational work. He further advanced this approach by predicting internal quality indices—such as soluble solids concentration and moisture content—in oriental melons using visible and red-near-infrared spectral bands, also garnering 22 citations. In the realm of robotics, Lee implemented a 3D location detection embedded system for tomato harvesting robots, leveraging deep learning and stereo vision on low-power NVIDIA Jetson hardware. His research addresses critical challenges in modern agriculture, including labor shortages and the need for automation in greenhouse environments. With a growing citation record and a clear focus on practical, deployable solutions, Lee is establishing himself as a key contributor to smart farming technologies that promise to enhance efficiency and sustainability in crop production.
Research Focus
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Top Papers
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