**4. Discussion**

### *4.1. Link between Spectral Reflectance and Plant Disease*

Multispectral or hyperspectral RS technologies based on near-ground, low-altitude UAV and satellite platforms offer multiple opportunities to improve the productivity of agricultural production systems and provide an automatic and objective alternative to the visual assessment of plant diseases [58–60]. The utility of RS techniques in the field of plant disease detection has been well documented, and the potential of spectral sensors in the detection of fungal diseases has been proven [61–63]. In the present study, at Anth values below 0.58, the difference in the reflectance characteristics between red and healthy leaves in the visible range was small, and the spectral characteristics were similar; this result is consistent with the lack of significant differences in the spectra of MDMV-infected and healthy leaves of corn exhibiting mild mosaic symptoms in a study by Beverly [30]. At the Anth values exceeding 0.58, however, the spectra of MDMV-infected and healthy leaves differed significantly in both visible and near-infrared regions, which is also consistent with Beverly's speculation that the near-infrared region is critical for studying MDMV [30].

However, in order to effectively use spectral reflectance measurements for disease detection, the key is to identify the most important spectral wavelengths that are closely linked to a particular disease. Only a few bands of the reflectance spectrum are of interest depending on the type of disease and the range of application. In the present study, bands closely linked to MDMV infection were mainly concentrated at 611–743 nm, with the maximum correlation coefficient of 0.76.

### *4.2. Application of Vegetation Indices Based on Two Arbitrary Bands*

Vegetation indices have been commonly used for analyzing and detecting changes in plant physiology and biochemistry. These indices, based on information at specific wavelengths, have been developed to reflect diverse plant parameters, such as pigment content, water content, and leaf area. Moreover, vegetation indices can be used as a measure of plant diseases. However, the quantitative analysis or identification of a specific disease based on the commonly used vegetation indices is not possible at present due the lack of disease specificity of the available indices. Therefore, we combined different wavelengths to construct vegetation indices (VIc) for simplifying disease detection by using spectral sensors, since each disease influences a spectral signature in a characteristic manner. In the present study, compared with the commonly used vegetation indices, the newly developed VIc showed improved classification accuracy for the red leaf spectrum (Table 3) and enhanced monitoring accuracy for MDMV severity (Figure 9). Inoue et al. [21] constructed a vegetation index based on two-band combinations for monitoring rice canopy nitrogen content and found that the RSI (D740, D522) model constructed based on the firstorder differential spectra at 740 and 522 nm showed better performance. Mahlein et al. [64] created contour maps of correlation coefficients for the disease severity of *Cercospora* leaf spot, rust, and powdery mildew in beet based on the NDVI of two arbitrary bands to identify and monitor plant diseases. The strongly correlated bands used for NDVI in their study were mainly concentrated at 500–500 nm, close to the optimal bands for NDVIr based on VIc in the present study.

### *4.3. RS-Based Identification of MDMV-Infected Leaves*

In the present study, among the LDA and SVM models, the classification model based on VIc showed the highest accuracy, followed by the models based on VIs and Rλ. Additionally, in the classification models based on VIs, SVM performed better than LDA, which is consistent with the trends reported by Shi et al. in the identification of wheat stripe rust, by Shang et al. in the classification of Australian virgin forest species, and by Calderón et al. in the early identification of olive *Verticillium* wilt [65–67]. SVM classifiers are constructed based on the threshold discriminant rules, which map samples to an appropriate feature space and can solve nonlinear and small-sample classification problems well. Both LDA and SVM classification models based on Rλ showed low accuracy mainly because Rλ offers very little spectral information for accurately identifying the target.

### *4.4. Application of Machine Learning Algorithms in Precision Agriculture*

The major RS-based estimation methods in plant physiology and biochemistry include the physical radiative transfer models and empirical statistical models [68–70]. The physical model simulates the reflectivity of a leaf blade based on a limited number of variables according to different mathematical and physical principles. The greatest advantages of this approach are that it is based on the radiative transmission model of electromagnetic waves and the ecological theory of vegetation and that it is not affected by vegetation type and other factors. However, despite being powerful, these models require a large amount of local perception data related to biotic and abiotic factors for calibration [71], thus limiting their applicability in the large-scale monitoring of crop growth [72].

Most empirical statistical models are constructed based on vegetation indices, and they are relatively simple and diverse in structure. However, these models are susceptible to vegetation type, light conditions, and canopy structure and are sensitive to soil background; thus, the universality of these models is poor. Among the empirical statistical models, the machine learning models have the advantage of realizing the high-precision prediction of leaf pigments by analyzing the relationship between leaf nutrient drivers and pigment content, without relying on specific crop parameters. Moreover, with the advantages of RS technology, some machine learning algorithms can assess crop growth at a regional scale from satellite images. However, the inversion results obtained by linear models are typically not reliable, and nonlinear machine learning algorithms, such as MLR, PLSR, PCA, SVM, and RF, can analyze complex relationships between vegetation indices and multiple factors of crop growth [73,74]. In the present study, the MLR, PCR, PLSR, and SVMR models based on machine learning algorithms showed significant differences in their accuracy with different modeling parameters; however, the predictive performance of the four models was satisfactory, as evidenced by the high R<sup>2</sup> c and R<sup>2</sup> v values, and their accuracy was significantly higher than that of the simple regression model.

Rapid and large-scale monitoring of crop diseases can effectively reduce the pressure on plant protectors and is an important means to prevent and control diseases and to ensure food health. In addition, such efforts have made significant contributions in eliminating hunger, ensuring global food security, improving nutrition, and achieving sustainable development goals for food proposed by the United Nations [75].
