Improving QGA-ELM Inversion Model of Rice Leaf Area Index Based on UAV Remote Sensing Image

The leaf area index (LAI) is an important physiological parameter that characterizes the growth of crops. Traditional measurement could not meet the demands of large-scale accurate monitoring. QGA-ELM and LS-SVM algorithm combined with UAV remote sensing images was used to achieve the goal of building large-scale fast inversion modeling of LAI in this paper. Linear and nonlinear models were constructed for comparing the correlation between six spectral indices and LAI by categorizing the nitrogen level. The LS-SVM model was constructed to replace traditional linear model, the determination coefficient of correction set and prediction set (R2C and R2P) were 0.6496 and 0.6814; and the root mean square error of correction set and prediction set (RMSEC and RMSEP) were 0.5702 and 0.6842, respectively. The results showed that the inversion of edge objects in noncrop areas was not so stable. In order to address the problem, an improvement based on the extreme learning machine (ELM) and quantum genetic algorithm (QGA) with probabilistic evolution were used to combine with LS-SVM for overcoming the problem which the hidden layer connection weight and threshold randomly generated and solve the problems of slow regression of nonlinear data and insufficient model generalization ability. Compared with traditional linear and nonlinear regression, the QGA-ELM combined with LS-SVM showed the following: (1) improving the optimization ability greatly and avoid the prematurity of GA (genetic algorithm) effectively. The generalization performance has also been enhanced. (2) R2P of prediction set was 0.6686, and RMSEP was 0.8952 which could reflect the growth and distribution trend of rice in the regional scale. (3) Adapting different fertilization gradients (deficiency to excess) could provide basis for LAI inversion in different varieties and accumulated temperature zone of rice. The results above showed that QGA-ELM combined with LS-SVM could improve the stability of the model greatly and provide reference significance for rice growth inversion.