文献类型： 外文期刊

作者： Qiu Hong-lin ¹ ; Liu Tian-yuan ¹ ; Kong Li-li ¹ ; Yu Xin-na ¹ ; Wang Xian-da ² ; Huang Mei-zhen ¹ ;

作者机构： 1.Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai 200240, Peoples R China

2.Fujian Acad Agr Sci, Fruit Res Inst, Fuzhou 350013, Peoples R China

3.Shanghai Univ Engn Sci, Sch Math Phys & Stat, Shanghai 201620, Peoples R China

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

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.7； 五年影响因子：0.6 ）

ISSN： 1000-0593

年卷期： 2024 年 44 卷 6 期

页码：

收录情况： SCI

摘要： 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 OH 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 categorieshealthy, mild disease, severe disease, and magnesium deficiency-using qPCR determination. Reflecting on the characteristics of leaf samples in the visible light band (450 similar to 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.