**5. Conclusions**

The key to the effective identification of MDMV based on spectral reflectance is to find the appropriate wavelength that is closely linked to the disease. In the present study, we first analyzed the spectral differences between red and healthy leaves and determined the band region of the observed spectral difference between the two types of leaves. Next, by comparing the spectra of leaves with different severities of MDMV infection (as indicated by different Anth values), we obtained the variation rule of the reflectance spectra for MDMV infection severity and the band with the strongest correlation with Anth (Rλ). However, single-band spectra are not sufficient for fully characterizing plants. Therefore, in order to detect plant growth statuses, we selected 12 vegetation indices that are closely correlated with Anth for MDMV monitoring. Following the construction principle of NDVI, RVI, DVI, and SAVI, we constructed VIc based on the combination two arbitrary bands. Furthermore, we constructed LDA and SVM classification models for MDMV based on Rλ, VIs, and VIc and identified a classification model that could accurately distinguish red leaves from the healthy ones. In addition, we constructed the Anth regression model based on three spectral parameters in order to accurately assess the severity of MDMV infection. The major conclusions are as follows:

(1) The spectral differences between red and healthy leaves were mainly concentrated in the 493–764 nm region, and the maximum difference was recorded near 700 nm.


**Author Contributions:** Conceptualization, L.L.; methodology, L.L.; software, L.L.; validation, L.L., Q.C.; formal analysis, L.L.; investigation, L.L.; resources, Q.C.; data curation, L.L., Q.W. and Y.H.; writing—original draft preparation, L.L.; writing—review and editing, L.L..; visualization, L.L.; supervision, Q.C.; project administration, Q.C.; funding acquisition, Q.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National High Technology Research and Development Program of China (863 Program), gran<sup>t</sup> number 2013AA102401-2.

**Institutional Review Board Statement:** This study not involving humans.

**Informed Consent Statement:** This study not involving humans.

**Data Availability Statement:** Data sharing is not application to this article.

**Acknowledgments:** We would like to thank all the students in Chang's team for collecting the data for us, as well as the managing editors and anonymous reviewers for their constructive comments, which greatly improved the quality of this paper.

**Conflicts of Interest:** The authors declare no conflict of interest.
