Rapid Detection of Citrus Huanglongbing Based on Extraction of
Characteristic Wavelength of Visible Spectrum and
Classification Algorithm
QIU Hong-lin1, LIU Tian-yuan1*, KONG Li-li1, 3, YU Xin-na1, WANG Xian-da2*, HUANG Mei-zhen1
1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240，China
2. Fruit Research Institute, Fujian Academy of Agricultural Sciences, Fuzhou 350013，China
3. School of Mathematics, Physics & Statistics, Shanghai University of Engineering Science, Shanghai 201620，China
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
邱鸿霖，刘天元，孔丽丽，于新娜，王贤达，黄梅珍. 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[J]. 光谱学与光谱分析, 2024, 44(06): 1518-1525.
QIU Hong-lin, LIU Tian-yuan, KONG Li-li, YU Xin-na, WANG Xian-da, HUANG Mei-zhen. Rapid Detection of Citrus Huanglongbing Based on Extraction of
Characteristic Wavelength of Visible Spectrum and
Classification Algorithm. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1518-1525.
[1] SHAN Zhen-ju, GUO Heng, FENG Zhen, et al(单振菊, 郭 恒, 冯 震, 等). Journal of Zhongkai University of Agriculture and Technology(仲恺农业技术学院学报), 2005，(4): 45.
[2] Bové J M. Phytoparasitica, 2014, 42(5): 579.
[3] Bové J M. Journal of Plant Pathology, 2006, 88(1): 7.
[4] WANG Ai-min, DENG Xiao-ling(王爱民, 邓晓玲). Gaungdong Agricultural Sciences(广东农业科学), 2008, (6): 101.
[5] MA Hao, JI Hai-yan, Won Suk Lee(马 淏, 吉海彦, Won Suk Lee). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(10): 2713.
[6] MEI Hui-lan, DENG Xiao-ling，HONG Tian-sheng, et al(梅慧兰, 邓小玲, 洪添胜, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(9): 140.
[7] LIU Yan-de, XIAO Huai-chun, SUN Xu-dong, et al(刘燕德, 肖怀春, 孙旭东, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2018, 34(3): 180.
[8] Sankaran S, Ehsani R, Etxeberria E. Talanta, 2010, 83(2): 574.
[9] Li Hongdong, Liang Yizeng, Xu Qingsong, et al. Analytica Chimica Acta, 2009, 648(1): 77.
[10] Mirjalili S, Mirjalili S M, Lewis A. Advances in Engineering Software, 2014, 69: 46.
[11] Li Wenbin, Hartung J S, Levy L. Journal of Microbiological Methods, 2006, 66: 104.
[12] WANG Xian-da, HU Han-qing, LIN Xiong-jie, et al(王贤达, 胡菡青, 林雄杰, 等). South China Fruits(中国南方果树), 2016, 45(6): 6.
[13] FAN Guo-cheng, LIU Bo, WU Ru-jian, et al(范国成，刘 波, 吴如健, 等). Fujian Journal of Agricultural Sciences(福建农业学报), 2009, 24(2): 183.
[14] Leardi R. Journal of Chemometrics, 2000, 14(5-6): 643.
[15] Deng S, Yeh T H. Expert Systems with Applications, 2010, 37(12): 8417.
[16] Suykens J A K, Gestel T V, Brabanter J D, et al. Least Squares Support Vector Machines. 1st ed. World Scientific, 2002.
[17] XU Hui-di, LIN Lu-lu, HUANG Mei-zhen, et al(徐荟迪, 林露璐, 黄梅珍, 等). Acta Optica Sinica(光学学报), 2019, 39(3): 388.
