Early diagnosis of citrus Huanglongbing by Raman spectroscopy and machine learning

文献类型： 外文期刊

第一作者： Kong, Lili

作者： Kong, Lili;Liu, Tianyuan;Qiu, Honglin;Yu, Xinna;Huang, Meizhen;Wang, Xianda;Kong, Lili;Huang, Zhiwei

作者机构：

关键词： Raman spectroscopy; Huanglongbing; detection strategy; machine learning; early diagnosis

期刊名称：LASER PHYSICS LETTERS （ 影响因子：1.7； 五年影响因子：1.5 ）

ISSN： 1612-2011

年卷期： 2024 年 21 卷 1 期

页码：

收录情况： SCI

摘要： Timely diagnosis of citrus Huanglongbing (HLB) is fundamental to suppressing disease spread and reducing economic losses. This paper explores the combination of Raman spectroscopy and machine learning for on-site, accurate and early diagnosis of citrus HLB. The tissue lesion characteristics of citrus leaves at different stages of HLB infection was explored by Raman spectroscopy, and a scientific spectral acquisition strategy was proposed. Combined with machine learning for feature extraction, modeling learning, and predictive analysis, the diagnostic accuracies of principal component analysis (PCA)-Partial least-square and PCA-support vector machine models for the prediction set were 94.07% and 95.56%, respectively. Compared with conventional random detection method, the detection strategy proposed in this paper shows higher accuracy, especially in early HLB diagnosis with significant advantages.

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