Miguel Cabanillas-Lazo, Jerry K Benites-Meza, Liseth Pinedo-Castillo, Eliana L Fernandez-Quiroz, Andres Pacherres-Lopez, Percy Herrera-Añazco, Vicente A Benites-Zapata
Association between self-perceived consultation time and understanding of the prescribed treatment: An analysis of a national survey in Peru
Patient education and counseling
sunflower important crop vitality moisture content sunflower seeds important influence sunflowers planting yield employing hyperspectral technology spectral characteristics sunflower seeds within wavelength range 3841034 nm carefully analyzed aim achieving effective prediction seed vitality moisture content firstly original hyperspectral data subjected preprocessing techniques savitzkygolay smoothing standard normal variable correction snv multiplicative scatter correction msc effectively reduce noise interference ensuring accuracy reliability data subsequently principal component analysis pca extreme gradient boosting xgboost stacked autoencoders sae utilized extract key feature bands enhancing interpretability predictive performance data modeling phase random forests rfs lightgbm algorithms separately employed construct classification models seed vitality prediction models moisture content experimental results demonstrated sgsaelightgbm model exhibited outstanding performance classification task sunflower seed vitality achieving accuracy rate 9865 meanwhile snvxgboostlightgbm model showed remarkable achievement moisture content prediction coefficient determination r2 09715 root mean square error rmse 08349 conclusion study confirms fusion hyperspectral technology multivariate data analysis algorithms enables accurate rapid assessment sunflower seed vitality moisture content providing robust tools theoretical support seed quality evaluation agricultural production practices furthermore research expands application hyperspectral technology unraveling intrinsic vitality characteristics sunflower seeds also possesses significant theoretical practical value