SiPLS-CARS与GA-ELM对哈密瓜冠层叶片含水率的反演估测

doi:10.3964/j.issn.1000-0593(2022)08-2565-07

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Abstract To realize more precise irrigation management during the growing period of Hami Melon in the field. The traditional methods for measuring leaf moisture content are inefficient, complicated and destructive, which is not conducive to obtaining moisture content of Hami melon leaves in the field. In this study, the leaf samples of cantaloupe in four periods of growth (M₁), flowering (M₂), fruit (M₃) and maturity (M₄) were obtained by spectral technology, and the moisture content of the leaf samples was measured by drying method. The influence of the choice of kernel function and the number of hidden neurons on the precision of the ELM model is discussed. Then SiPLS and its combined algorithm with CARS, GA and SPA were used to extract the characteristic wavelengths with a high correlation with leaf moisture content. GA and PSO algorithms are used to optimize the connection weights (W) between the input layer and the hidden layer of the ELM model, and the threshold (B) of the hidden layer of the ELM model, the optimal and stable W and B values are obtained further to improve the stability and prediction accuracy of the model. Finally, four feature wavelength extraction algorithms are combined with ELM, GA-ELM and PSO-ELM to analyze the model, and the Correlation Coefficient between the correction set and the prediction set is taken as the evaluation index of the model. Through the comparison and analysis, the inversion estimation model of cantaloupe canopy leaf moisture content was optimized. The results show that the number of SiPLS and its combination with CARS, GA and SPA are 273, 20, 32 and 6 respectively, accounting for 15.6%, 1.2%, 1.9% and 0.03% of the total spectrum variables. Taking the selected characteristic wavelength as the independent variable and the moisture content of the leaves as the dependent variable, the prediction model of ELM is established, but the prediction accuracy is not very ideal. Therefore, GA and PSO are introduced to optimize the randomly generated W and B values in ELM. Finally, it is found that the precision of predicting water content of cantaloupe canopy leaves based on the ELM model optimized by GA and SiPLS-CARS is the best. Therefore, the optimal modeling method of leaf moisture content retrieval is SiPLS-CARS-GA-ELM, R_C value is 0.928 9, R_P value is 0.903 2, the precision of the model is high, which can be used to detect the leaf moisture content in cantaloupe canopy, the research provides the theoretical basis for the field irrigation management.

Key words：Hami Melon Leaf moisture content; Model optimization; Feature Variable Selection; Genetic Algorithm; Particle Swarm Optimization; ELM model

Received: 2021-05-30 Accepted: 2021-11-02

ZTFLH:

S565.1

Corresponding Authors: GUO Jun-xian E-mail: junxianguo@163.com

Cite this article:

GUO Yang,GUO Jun-xian,SHI Yong, et al. Estimation of Leaf Moisture Content in Cantaloupe Canopy Based on SiPLS-CARS and GA-ELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2565-2571.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2022)08-2565-07 OR https://www.gpxygpfx.com/EN/Y2022/V42/I08/2565

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