Papers
2
Total Citations
44
H-Index
2
About
Youngki Hong is a leading researcher in precision agriculture and non-destructive quality assessment, specializing in the application of hyperspectral imaging and machine learning for automated crop monitoring. His major contributions focus on developing snapshot-type hyperspectral imagery combined with advanced classifiers, such as support vector machines, to rapidly and accurately determine fruit maturity and internal quality indices. Notably, his work on classifying tomato maturity using hyperspectral data and support vector classifiers has garnered 22 citations, addressing critical needs for automation in hydroponic greenhouses amid labor shortages and pandemic challenges. Similarly, his research on oriental melons achieved 22 citations by successfully predicting solid solutions concentration and moisture content using visible and red-near-infrared spectral bands. Hong’s innovative integration of machine learning models with compact, snapshot-type sensors enables real-time, non-destructive quality control, paving the way for agricultural robots and smart farming solutions. His achievements highlight a commitment to solving real-world agricultural problems, making his work highly influential for students and researchers exploring sustainable, technology-driven farming practices.
Research Focus
Key Achievements
Top Papers
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