Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements
Prompt monitoring of maize dwarf mosaic virus (MDMV) is critical for the prevention and control of disease and to ensure high crop yield and quality. Here, we first analyzed the spectral differences between MDMV-infected red leaves and healthy leaves and constructed a sensitive index (SI) for measur...
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2021
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oai:doaj.org-article:1d3ad893f1f44a4db9ce2f5ffdf4688b2021-11-25T18:54:19ZIdentification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements10.3390/rs132245602072-4292https://doaj.org/article/1d3ad893f1f44a4db9ce2f5ffdf4688b2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4560https://doaj.org/toc/2072-4292Prompt monitoring of maize dwarf mosaic virus (MDMV) is critical for the prevention and control of disease and to ensure high crop yield and quality. Here, we first analyzed the spectral differences between MDMV-infected red leaves and healthy leaves and constructed a sensitive index (SI) for measurements. Next, based on the characteristic bands (R<sub>λ</sub>) associated with leaf anthocyanins (Anth), we determined vegetation indices (VI<sub>s</sub>) commonly used in plant physiological and biochemical parameter inversion and established a vegetation index (VI<sub>c</sub>) by utilizing the combination of two arbitrary bands following the construction principles of NDVI, DVI, RVI, and SAVI. Furthermore, we developed classification models based on linear discriminant analysis (LDA) and support vector machine (SVM) in order to distinguish the red leaves from healthy leaves. Finally, we performed UR, MLR, PLSR, PCR, and SVM simulations on Anth based on R<sub>λ</sub>, VI<sub>s</sub>, VI<sub>c</sub>, and R<sub>λ</sub> + VI<sub>s</sub> + VI<sub>c</sub> and indirectly estimated the severity of MDMV infection based on the relationship between the reflection spectra and Anth. Distinct from those of the normal leaves, the spectra of red leaves showed strong reflectance characteristics at 640 nm, and SI increased with increasing Anth. Moreover, the accuracy of the two VI<sub>c</sub>-based classification models was 100%, which is significantly higher than that of the VI<sub>s</sub> and R<sub>λ</sub>-based models. Among the Anth regression models, the accuracy of the MLR model based on R<sub>λ</sub> + VI<sub>s</sub> + VI<sub>c</sub> was the highest (R<sup>2</sup><sub>c</sub> = 0.85; R<sup>2</sup><sub>v</sub> = 0.74). The developed models could accurately identify MDMV and estimate the severity of its infection, laying the theoretical foundation for large-scale remote sensing-based monitoring of this virus in the future.Lili LuoQingrui ChangQi WangYong HuangMDPI AGarticleplant diseaseband selectionmachine learninganthocyaninhyperspectral reflectancelinear discriminant analysisScienceQENRemote Sensing, Vol 13, Iss 4560, p 4560 (2021) |
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plant disease band selection machine learning anthocyanin hyperspectral reflectance linear discriminant analysis Science Q |
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plant disease band selection machine learning anthocyanin hyperspectral reflectance linear discriminant analysis Science Q Lili Luo Qingrui Chang Qi Wang Yong Huang Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements |
| description |
Prompt monitoring of maize dwarf mosaic virus (MDMV) is critical for the prevention and control of disease and to ensure high crop yield and quality. Here, we first analyzed the spectral differences between MDMV-infected red leaves and healthy leaves and constructed a sensitive index (SI) for measurements. Next, based on the characteristic bands (R<sub>λ</sub>) associated with leaf anthocyanins (Anth), we determined vegetation indices (VI<sub>s</sub>) commonly used in plant physiological and biochemical parameter inversion and established a vegetation index (VI<sub>c</sub>) by utilizing the combination of two arbitrary bands following the construction principles of NDVI, DVI, RVI, and SAVI. Furthermore, we developed classification models based on linear discriminant analysis (LDA) and support vector machine (SVM) in order to distinguish the red leaves from healthy leaves. Finally, we performed UR, MLR, PLSR, PCR, and SVM simulations on Anth based on R<sub>λ</sub>, VI<sub>s</sub>, VI<sub>c</sub>, and R<sub>λ</sub> + VI<sub>s</sub> + VI<sub>c</sub> and indirectly estimated the severity of MDMV infection based on the relationship between the reflection spectra and Anth. Distinct from those of the normal leaves, the spectra of red leaves showed strong reflectance characteristics at 640 nm, and SI increased with increasing Anth. Moreover, the accuracy of the two VI<sub>c</sub>-based classification models was 100%, which is significantly higher than that of the VI<sub>s</sub> and R<sub>λ</sub>-based models. Among the Anth regression models, the accuracy of the MLR model based on R<sub>λ</sub> + VI<sub>s</sub> + VI<sub>c</sub> was the highest (R<sup>2</sup><sub>c</sub> = 0.85; R<sup>2</sup><sub>v</sub> = 0.74). The developed models could accurately identify MDMV and estimate the severity of its infection, laying the theoretical foundation for large-scale remote sensing-based monitoring of this virus in the future. |
| format |
article |
| author |
Lili Luo Qingrui Chang Qi Wang Yong Huang |
| author_facet |
Lili Luo Qingrui Chang Qi Wang Yong Huang |
| author_sort |
Lili Luo |
| title |
Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements |
| title_short |
Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements |
| title_full |
Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements |
| title_fullStr |
Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements |
| title_full_unstemmed |
Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements |
| title_sort |
identification and severity monitoring of maize dwarf mosaic virus infection based on hyperspectral measurements |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/1d3ad893f1f44a4db9ce2f5ffdf4688b |
| work_keys_str_mv |
AT lililuo identificationandseveritymonitoringofmaizedwarfmosaicvirusinfectionbasedonhyperspectralmeasurements AT qingruichang identificationandseveritymonitoringofmaizedwarfmosaicvirusinfectionbasedonhyperspectralmeasurements AT qiwang identificationandseveritymonitoringofmaizedwarfmosaicvirusinfectionbasedonhyperspectralmeasurements AT yonghuang identificationandseveritymonitoringofmaizedwarfmosaicvirusinfectionbasedonhyperspectralmeasurements |
| _version_ |
1718410574403993600 |