| In order to quickly and accurately estimate plant height and leaf area index(LAI) phenotypic characters of wheat varieties(lines), the LAI estimation model and spectral index estimation model of plant height at different growth periods were constructed based on the digital orthophoto map(DOM) and digital surface model(DSM). With the help of single linear regression, multiple stepwise regression(SMLR) and partial least squares regression(PLSR) analyses, the comprehensive evaluation indices of determination coefficient(r), root mean square error(RMSE) and normalized root mean square error(nRMSE), the best model for plant height and LAI estimation in different growth periods was selected. The results showed that the model of plant height estimation in the whole growth period had the best effect, and its predicted value of plant height was highly fitted with the measured value(r, RMSE and nRMSE were 0.87, 5.90 cm and 9.29%, respectively); in each growth period, the prediction accuracy of the model in the filling period was better, while that at maturity was the worst; r was 0.79 and 0.69, respectively. The correlation coefficients of BGRI, RGBVI, NRI and NGRDI were significant, and the three regression estimation models in each period show high stability and fitting effect, among which SMLR regression model had the best prediction accuracy for LAI in each growth period, and its prediction of jointing, booting, flowering, filling and maturity periods is the best; r was 0.68, 0.57, 0.61, 0.68 and 0.53, respectively. This shows that it is feasible to extract plant height from DSM images of wheat at different growth stages obtained by UAV and build LAI estimation model by using 18 spectral indices. |
基本信息
