文献类型： 外文期刊

作者： Kong, Lili ¹ ; Liu, Tianyuan ¹ ; Qiu, Honglin ¹ ; Yu, Xinna ¹ ; Wang, Xianda ² ; Huang, Zhiwei ⁴ ; Huang, Meizhen 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

4.Natl Univ Singapore, Coll Design & Engn, Singapore 117576, Singapore

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.