Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Abstract

During the growth season, jujube trees are susceptible to infestation by the leaf mite, which reduces the fruit quality and productivity. Traditional monitoring techniques for mites are time-consuming, difficult, subjective, and result in a time lag. In this study, the method based on a particle swarm optimization (PSO) algorithm extreme learning machine for estimation of leaf chlorophyll content (SPAD) under leaf mite infestation in jujube was proposed. Initially, image data and SPAD values for jujube orchards under four severities of leaf mite infestation were collected for analysis. Six vegetation indices and SPAD value were chosen for correlation analysis to establish the estimation model for SPAD and the vegetation indices. To address the influence of colinearity between spectral bands, the feature band with the highest correlation coefficient was retrieved first using the successive projection algorithm. In the modeling process, the PSO correlation coefficient was initialized with the convergent optimal approximation of the fitness function value; the root mean square error (RMSE) of the predicted and measured values was derived as an indicator of PSO goodness-of-fit to solve the problems of ELM model weights, threshold randomness, and uncertainty of network parameters; and finally, an iterative update method was used to determine the particle fitness value to optimize the minimum error or iteration number. The results reflected that significant differences were observed in the spectral reflectance of the jujube canopy corresponding with the severity of leaf mite infestation, and the infestation severity was negatively correlated with the SPAD value of jujube leaves. The selected vegetation indices NDVI, RVI, PhRI, and MCARI were positively correlated with SPAD, whereas TCARI and GI were negatively correlated with SPAD. The accuracy of the optimized PSO-ELM model (R2 = 0.856, RMSE = 0.796) was superior to that of the ELM model alone (R2 = 0.748, RMSE = 1.689). The PSO-ELM model for remote sensing estimation of relative leaf chlorophyll content of jujube shows high fault tolerance and improved data-processing efficiency. The results provide a reference for the utility of UAV remote sensing for monitoring leaf mite infestation of jujube.

Study on Hyperspectral Reflectance of Brown Planthopper (Nilaparvata lugens) Infestation in Rice

“…The same trend followed in our study; the healthy plants showed higher reflectance radiation in NIR region than the BPH-infected plants. The Previous study of Lu et al (2022) also revealed similar spectral signature for leaf mite infestation in the crop jujube. A similar trend was pursued in the earlier study of Riedell and Blackmer (1999) observed in wheat seedlings with aphids (Diuraphis noxia) and greenbugs (Schizaphis graminum).…”

Inverting Chlorophyll Content in Jujube Leaves Using a Back-Propagation Neural Network–Random Forest–Ridge Regression Algorithm with Combined Hyperspectral Data and Image Color Channels

“…For example, Jay et al have demonstrated the potential of vegetation indices in high-throughput phenotypic analyses by conducting inversion of LAI, chlorophyll, and nitrogen content in sugar beet crops using multi-source data [53]. Additionally, Lu et al have provided practical insights for the remote monitoring of jujube mite infestation by inverting jujube leaf chlorophyll content under spider mite stress using ensemble algorithms [54]. Yuzhen Wu et al used unmanned aerial vehicle hyperspectral remote sensing to assess the multi-parameter health of jujube trees and achieved R 2 values of 0.726 and a maximum of 0.853 using the RF method [55].…”

A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine

“…PSO is a population-based intelligent optimization stochastic search algorithm ( Lu et al., 2022 ). First, the initialized particle swarm is generated as a solution in the feasible space.…”