Detection of Anthracnose in Camellia Oleifera Based on Laser-Induced Breakdown Spectroscopy

Yan-de, 刘燕德 Liu; Xue, 高雪 Gao; Meng-jie, 程梦杰 Cheng; Zhaoguo, 侯兆国 Hou; Xiaodong, 林晓东 Lin; Jia, 徐佳 Xu

doi:10.3788/lop57.093006

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, there are two major aspects to detect plant diseases [ 2 ]. One is laboratory testing techniques, Shuai Xiao-chun [ 3 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Research on anthracnose grade of Camellia oleifera based on the combined LIBS and THz technology

Li¹,

Qiu²,

Zhan³

et al. 2022

Plant Methods

View full text Add to dashboard Cite

Background Anthracnose of Camellia oleifera is a very destructive disease that commonly occurs in the Camellia oleifera industry, which severely restricts the development of the Camellia oleifera industry. In the early stage of the Camellia oleifera suffering from anthracnose, only the diseased parts of the tree need to be repaired in time. With the aggravation of the disease, the diseased branches need to be eradicated, and severely diseased plants should be cut down in time. At present, aiming at the problems of complex experiments and low accuracy in detecting the degree of anthracnose of Camellia oleifera, a method is proposed to detect the degree of anthracnose of Camellia oleifera leaves by using terahertz spectroscopy (THz) combined with laser-induced breakdown spectroscopy (LIBS), so as to realize the rapid, efficient, non-destructive and high-precision determination of the degree of anthracnose of Camellia oleifera. Results Mn, Ca, Ca II, Fe and other elements in the LIBS spectrum of healthy and infected Camellia oleifera leaves with different degrees of anthracnose are significantly different, and the Terahertz absorption spectra of healthy Camellia oleifera leaves, and Camellia oleifera leaves with different degrees of anthracnose there are also significant differences. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and linear discriminant analysis (LDA) are used to establish the fusion spectrum anthracnose classification model of Camellia oleifera. Among them, the Root mean square error of prediction (RMSEP) and the prediction determination coefficient R2p of THz-LIBS-CARS-PLS-DA of prediction set are 0.110 and 0.995 respectively, and the misjudgment rate is 1.03%; The accuracy of the modeling set of THz (CARS)-LIBS (CARS)-SVM is 100%, and the accuracy of prediction set is 100%, after preprocessing of the multivariate scattering correction (MSC), the accuracy of the THz-LIBS-MSC-CARS modeling set is 100%, and the accuracy of prediction set is 100%; The accuracy rate of THz-LIBS-MSC-CARS-LDA of modeling set is 98.98%, and the accuracy rate of the prediction set is 96.87%. Conclusion The experimental results show that: the SVM model has higher qualitative analysis accuracy and is more stable than the PLS-DA and LDA models. The results showed that: the THz spectrum combined with the LIBS spectrum could be used to separate healthy Camellia oleifera leaves from various grades of anthracnose Camellia oleifera leaves non-destructively, quickly and accurately.

show abstract

“…At present, there are two major aspects to detect plant diseases [ 2 ]. One is laboratory testing techniques, Shuai Xiao-chun [ 3 ], etc.…”

Section: Introductionmentioning

confidence: 99%