ScholarWorks@Gyeongsang National University: Ryu, Chan Seok

Detailed Information

Ryu, Chan Seok (유찬석)

농업생명과학대학 (생물산업기계공학과)

Research Area

Tel 0557721897
Homepage

ResearcherID ORCiD Scopus Google Scholar ID ResearchGate

Keyword

Hyperspectral imagery:6||UAV:3||Machine learning:3||Groundbased hyperspectral imaging:3||Vegetation indices:3||apple tree:2||Remote sensing:2||Effective accumulated temperature:2||Salinity:2||Full width at half maximum:2||SegNet:2||hyperspectral imaging:2||unmanned aerial vehicle:2||Simple linear regression:2||Paddy rice:2||Soybean:2||Band ratio:2||Multispectral image:2||Spring potato:2||UNet:2||Precision agriculture:2||UAV remote sensing:2||Prediction model:2||ANN:2||Image processing:2||Chinese cabbage:2||Growth information:2||Decision tree:2||Lightshielding curtains:1||Linear regression model:1||Linear regression model Red pepper:1||MLR:1||Model simulation:1||Moisture content:1||Multispectral:1||Multispectral image sensor:1||Multispectral imagery:1||Nitrogen content:1||Nondestructive analysis:1||Onion:1||PLSRegression model:1||PLSVIP:1||Panicle initiation stage:1||Partial least squares regression:1||Pepper:1||Polyphenols:1||Protein content:1||RGB:1||Red pepper:1||Region of interest:1||Reproducibility:1||Seedling quality:1||Sensor:1||Sorghum:1||Sorghum Cultivation Area:1||Spectral band:1||Spectral reflectance:1||Spectrum:1||UAV Imagery:1||Unmanned Aerial Vehicle:1||Unmanned aerial vehicle:1||Vegetation Indices:1||YOLO:1||Yields:1||classification:1||decision tree:1||full width at half maximum:1||heading stage:1||hyperspectral image:1||hyperspectral imagery:1||hyperspectral reflectance:1||knearest neighbors:1||kappa coefficient:1||lasso regression:1||linear regression model:1||lowtemperature stress:1||macronutrients:1||methane fermentation digested liquid:1||model considered growing temperature:1||multispectral image:1||multispectral image sensor:1||multispectral imagery:1||mutual prediction:1||nitrogen content:1||optimal spectral band:1||overall accuracy:1||paddy rice:1||paddy soil, application timing:1||partial least square (PLS) regression:1||regression model:1||rice yield:1||riceprotein content:1||soil nitrogen:1||spectral merging:1||tomato seedling:1||total nonstructural carbohydrate:1||묘소질:1||무인기 영상:1||부분최소제곱회귀분석:1||비파괴분석:1||수수 재배지:1||영상 분할:1||영상처리:1||온실습도(Greenhouse humidity):1||온실온도(Greenhouse temperature):1||제어시스템(Control system):1||천창제어(Roof window control):1||포그냉방(Fog cooling):1||환기율(Ventilation rate):1||Antioxidant capacity:1||Asparagus officinalis L.:1||BMA:1||Binding properties:1||Catechin:1||Classification:1||Computational cost:1||Decisiontree classifier:1||Detection:1||Drought:1||FWHM:1||Fertilizer:1||Fire blight:1||Flavonoids:1||Fusarium wilt:1||Gaussian process regression:1||Grain protein:1||Green house:1||Green tea:1||Growing degreedays:1||Growing degreedays integrated model:1||Halogen:1||Harvest:1||Hyperspectral:1||Hyperspectral image:1||Image Segmentation:1||Knearest neighbors:1||

RSS_1.0 RSS_2.0 ATOM_1.0

Gyeongsang National University Central Library, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do, 52828, Republic of Korea+82-55-772-0533

Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.

Detailed Information

Keyword

BROWSE