基于可见光谱特征波长提取和分类算法的柑橘黄龙病快检研究

doi:10.3964/j.issn.1000-0593(2024)06-1518-08

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Abstract The Citrus Huanglongbing (HLB), caused by the Asian citrus psyllid, represents a severe disease with no current cure. Its control is of significant importance and economic value. Current diagnostic approaches utilizing the spectral differences between healthy and diseased leaves show promising applications. Diseased leaves exhibit notable differences from healthy ones in the chlorophyll reflection zone and the O—H stretching vibration region of the visible spectrum. With its low cost and simplicity in data collection and processing, the visible spectrum detection scheme presents a feasible and significant approach for the rapid detection of HLB. To reduce spectral data redundancy and computational load, achieving precise early identification of HLB and minimizing misdiagnosis of similar symptoms, this study collected 160 leaf samples from HLB-affected areas. These samples were classified into four categories—healthy, mild disease, severe disease, and magnesium deficiency-using qPCR determination. Reflecting on the characteristics of leaf samples in the visible light band (450~800 nm), the study involved preprocessing spectral data through S-G smoothing and down sampling. To select feature wavelengths that encapsulate maximum spectral information, Genetic Algorithm (GA), Successive Projections Algorithm (SPA), and Competitive Adaptive Reweighted Sampling (CARS) were employed for feature wavelength extraction and dimensionality reduction, further simplifying model complexity and enhancing prediction accuracy. Considering the generalization ability and detection speed, the study used the Least Squares Support Vector Machine (LS-SVM) and Random Forest (RF) to classify and discriminate the dimensionally-reduced data from the two variable selection algorithms. The best rapid detection scheme was selected by validating and optimising different models. -In comparison with others, the SPA-RF model achieved a discrimination accuracy of 100% and 97.5% for the training and test sets, respectively. The results demonstrate that the combination of SPA and RF in the classification model effectively accomplishes early pathological identification of HLB and distinguishes HLB-diseased leaves from similar symptoms, providing a basis for rapid detection and control of Citrus Huanglongbing.

Key words：Visible reflection spectra;Characteristic wavelength;Continuous projection algorithm; Random forest; Detection of Citrus Huanglongbing

Received: 2022-10-05 Accepted: 2024-01-04

ZTFLH:

O433

Corresponding Authors: LIU Tian-yuan, WANG Xian-da E-mail: tianyuanl@sjtu.edu.cn; 564944260@qq.com

Cite this article:

QIU Hong-lin,LIU Tian-yuan,KONG Li-li, et al. Rapid Detection of Citrus Huanglongbing Based on Extraction of Characteristic Wavelength of Visible Spectrum and Classification Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1518-1525.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2024)06-1518-08 OR https://www.gpxygpfx.com/EN/Y2024/V44/I06/1518

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