*2.3. Analytical Methods*

### 2.3.1. Definition of Sensitivity Index (SI)

We constructed an *SI* based on the ratio and difference algorithm to measure the spectral difference between healthy and red leaves. The closer *SI* is to 0, the smaller the spectral difference between the two leaves and vice versa. The specific formula is as follows:

$$SI = \frac{R\_{\lambda} - R\_{h}}{R\_{h}} \tag{1}$$

where *Rλ* is the reflectance of the red leaf spectrum at wavelength *λ*, and *Rh* is the average reflectance of the healthy spectrum at wavelength *λ*.

### 2.3.2. Construction of Vegetation Indices with Two Arbitrary Bands

Vegetation indices have been widely used in the RS-based estimation of plant growth parameters. A VI constituting several bands can effectively minimize the errors associated with sensor specifications, atmosphere, and background differences, thus enhancing the description of the observation target [35,36]. However, due to the lack of disease specificity of these indicators, the quantification or identification of specific diseases based on common vegetation indices is currently impossible. Therefore, we combined different wavelengths

to construct a VI (VIc) for simplifying the spectral detection of plant diseases. Specifically, we used the normalized difference vegetation index (*NDVI*), ratio vegetation index (*RVI*), differential vegetation index (*DVI*), and soil-adjusted vegetation index (*SAVI*), defined as follows:

$$NDVI = \begin{pmatrix} R\_i - R\_j \end{pmatrix} / \left(\mathbb{R}\_i + \mathbb{R}\_j\right) \tag{2}$$

$$RVI = \mathbb{R}\_i / \mathbb{R}\_j \tag{3}$$

$$DVI = R\_{\bar{i}} - R\_{\bar{j}} \tag{4}$$

$$SAVI = 1.5 \left( R\_{\bar{i}} - R\_{\bar{j}} \right) / \left( R\_{\bar{i}} + R\_{\bar{j}} + 0.5 \right) \tag{5}$$

where *Ri*and *Rj*are the reflectance at *i* and *j* nm over the entire reflectance spectrum.

### 2.3.3. Linear Discriminant Analysis (LDA) Classification Model

The LDA model uses a linear combination of features as the classification standard to project data from the higher-dimensional to the lower-dimensional space, while ensuring that the intra-class variance of each class after the projection is small but the mean difference between the classes is large. It can be used for both classification and dimension reduction and is better suited for the linear classification of smaller data volumes and fewer indicators. In the present study, 48 red leaf spectra and 240 healthy leaf spectra were randomly selected from all the spectra as the calibration set, and the remaining 24 red leaf spectra and 120 healthy leaf spectra were used as the validation set. The LDA model was built using The Unscrambler X 10.4.

### 2.3.4. Support Vector Machine (SVM) Classification Model

In principle, SVM uses a kernel function to project the spectral information of samples in higher-dimensional space, constructs the hyperplane with the largest classification interval, and then accurately identifies different types of samples. It is better suited for linearly indivisible sample data, specifically using relaxation variables and kernel functions. In the present study, C-SVM discriminant analysis was performed on data from the calibration and validation sets using The Unscrambler X 10.4. The kernel type was a radial basis function (RBF), and the penalty coefficient (C) was 1. We adopted a 10-fold cross validation during modeling in order to improve the stability of the classification model.
