*2.5. Monitoring Methods*

The main purpose of this research is to explore the feasibility of remote sensing monitoring of crop diseases by meteorological data information. In addition, because of the small sample size in this experiment, three commonly used methods of liner discriminant analysis (LDA), SVM, and ANN and regular parameter settings were selected to construct wheat yellow rust monitoring models.

LDA is a dimensionality reduction method based on the best classification effect [41], usually by finding a set of linear feature combinations to classify two or more targets. The primary idea is to find a linear combination of variables to maximize in-between variance and minimize within-class variance [41]. The LDA model was implemented using the Statistical Package for the Social Sciences (SPSS 20.0). The parameters were set as default value.

In the SVM classification algorithm, the primary idea is to determine an optimal decision boundary and maximize the distance of the closest samples in two categories as much possible across the boundary [42]. Using the radial basis function (RBF) as the kernel function for SVM classification exhibited superior performance in the case of inseparable linearity [17]. The key parameters of SVM are shown in Table 2. The model is trained and tested in the Matlab R2016 software.

ANNs can be described as parallel and complex computing systems composed of large numbers of interconnected simple processors (neurons, also called nodes) [43]. As an important data mining tool, ANNs have comprehensive mathematical mechanisms and have been applied in various fields of remote sensing, such as ground objects identification and change detection [18,43]. In this study, the vegetation indices and meteorological data were the ANN input parameters. The transfer functions of logarithm sigmoid (logsig) transfer and linear (purelin) were used to activate the hidden layers and weighted output layers, respectively [44,45]. The learning rule takes the approach of a gradient descent backpropagation (traingd) training function. The key parameters of ANN are shown in Table 2. We used the MATLAB R2016 software to run the ANN models.


**Table 2.** Key parameters used for support vector machine (SVM) and artificial neural network (ANN) classification.

> Once sensitive spectral features and meteorological features were identified, the three classification algorithms (LDA, SVM, and ANN) were used to establish a classification model for wheat yellow rust. According to the three methods, wheat yellow rust classification was conducted using the following datasets: case 1: spectral vegetation indices (containing single temporal VIs and two-temporal nVIs); case 2: a combination of spectral vegetation indices and meteorological information.
