Non-contact measurement of droplets concentration on the leaf based on Vis/NIR spectroscopy
Baijing Qiu, Lei Yin
Abstract
Baijing Qiu, Lei Yin
Abstract
A rapid, real-time, non-contact and nondestructive way to measure droplets on the leaf was put forward based on Vis/NIR spectroscopy, and the relationship between nondestructive Vis/NIRS measurement and droplets concentration was developed. The multivariable analysis concluding multiple linear regression, stepwise regression, principal component analysis and partial least squares(PLS) was applied to build the regression models, by comparision the accuracy of prediction models, selected the best models, The calibration model was estimated through the correlation coefficient (r) and the root mean square error of cross validation (RMSECV). Finally, partial least squares (PLS) was applied to establish the optimal model, the correlation coefficient of calibration set was 0.998, the root mean square error of cross-validation of calibration set was 0.102.
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A rapid, real-time, non-contact and nondestructive way to measure droplets on the leaf was put forward based on Vis/NIR spectroscopy, and the relationship between nondestructive Vis/NIRS measurement and droplets concentration was developed. The multivariable analysis concluding multiple linear regression, stepwise regression, principal component analysis and partial least squares(PLS) was applied to build the regression models, by comparision the accuracy of prediction models, selected the best models, The calibration model was estimated through the correlation coefficient (r) and the root mean square error of cross validation (RMSECV). Finally, partial least squares (PLS) was applied to establish the optimal model, the correlation coefficient of calibration set was 0.998, the root mean square error of cross-validation of calibration set was 0.102.
Key concepts: Partial least squares regression, Calibration, Correlation coefficient, Mean squared error, Principal component regression, Coefficient of determination, Cross-validation, Linear regression