부산대학교 도서관

Ground-based hyperspectral imaging was used for estimating the nitrogen content of rice plants at the heading stage. The images were separated into two parts: 1) the rice plant; and 2) other elements using the equation of “ GreenNDVI–NDVI .” ${\mbi{Ref}}_{\mbi{RICE}}$ was calculated as the ratio of the reflectance of the rice plant to that of a reference board. Partial least square (PLS) model using reflectance data (R-PLS model) and PLS model using reflectance and temperature data (RT-PLS) was constructed to compare the accuracy between them. RT-PLS model was developed to improve the accuracy of R-PLS model by considering the differences of weather condition among years. When the precision (${\mbi{R}}^{\bf 2}$) and accuracy [root-mean-square error (RMSE) and relative error (RE)] of each R-PLS model were evaluated for each year using twofold cross-validation, ${\mbi{R}}^{\bf 2}$ ranged from 0.42 to 0.81, RMSE ranged from 0.81 to ${\bf 1.13}\nbsp\hbox{\bf gm}^{\bf -2}$, and RE ranged from 10.1% to 11.8%. When R-PLS model of each year was used to predict the other years’ data to determine the predictive power, RMSE values were higher (ranging from 1.40 to ${\bf 5.82}\nbsp\hbox{\bf gm}^{\bf -2}$) than those in each year’s validation value due to over- or underestimation. When an R-PLS model based on the data of 2 years was fitted, RMSE ranged from 1.11 to ${\bf 4.15}\nbsp\hbox{\bf gm}^{\bf -2}$ and RE ranged from 13.7% to 42.8%. By contrast, in RT-PLS models, RMSE and RE fell to less than ${\bf 1.21}\nbsp\hbox{\bf gm}^{\bf -2}$ and 12.3%, respectively. Thus, a combination of reflectance and temperature data was useful for constructing a model of rice plant at the heading stage.