## 2 Materials and Methods
### Methods
#### 2.2.4 Traditional Regression Models  
Support vector regression (SVR) is a widely used machine learning algorithm renowned for its excellent generalization capabilities, making it suitable for high-dimensional problem solving [20]. Random forest (RF) is a non-linear and non-parametric ensemble learning method that constructs multiple decision trees during training. When RF is used for the purpose of function approximation or regression, it is called random forest regression (RFR) [47]. Gradient boosting (GB) is an ensemble learner for both regression  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & & **CNN3-1** & **CNN3-2** & **CNN3-3** & **CNN3-4** \\ \hline Input & & \(1\times 204\) & \(1\times 204\) & \(1\times 204\) & \(1\times 204\) \\ Convolution & 1 & 32 kernels in size \(1\times 3\) & 64 kernels in size \(1\times 3\) & 128 kernels in size \(1\times 3\) & 256 kernels in size \(1\times 3\) \\  & 2 & with average pooling & with average pooling & with average pooling & with average pooling \\  & 3 & 32 kernels in size \(1\times 3\) & 64 kernels in size \(1\times 3\) & 128 kernels in size \(1\times 3\) & 256 kernels in size \(1\times 3\) \\ Fully connected & 1 & 512 nodes, ReLU & 512 nodes, ReLU & 512 nodes, ReLU & 512 nodes, ReLU \\  & 2 & 32 nodes, ReLU & 32 nodes, ReLU & 32 nodes, ReLU & 32 nodes, ReLU \\ Output & & 1 node & 1 node & 1 node & 1 node \\ \hline \hline \end{tabular}
\end{table}
Table 4: CNN architectures with three convolutional layers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & & **CNN1-1** & **CNN1-2** & **CNN1-3** & **CNN1-4** \\ \hline Input & & \(1\times 204\) & \(1\times 204\) & \(1\times 204\) & \(1\times 204\) \\ Convolution & 1 & 32 kernels in size \(1\times 3\) & 64 kernels in size \(1\times 3\) & 128 kernels in size \(1\times 3\) & 256 kernels in size \(1\times 3\) \\ Fully Connected & 1 & 512 nodes, ReLU & 512 nodes, ReLU & 512 nodes, ReLU & 512 nodes, ReLU \\  & 2 & 32 nodes, ReLU & 32 nodes, ReLU & 32 nodes, ReLU & 32 nodes, ReLU \\ Output & & 1 node & 1 node & 1 node & 1 node \\ \hline \hline \end{tabular}
\end{table}
Table 2: CNN architectures with a single convolutional layer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & & **CNN1-1** & **CNN1-2** & **CNN1-3** & **CNN1-4** \\ \hline Input & & \(1\times 204\) & \(1\times 204\) & \(1\times 204\) & \(1\times 204\) \\ Convolution & 1 & 32 kernels in size \(1\times 3\) & 64 kernels in size \(1\times 3\) & 128 kernels in size \(1\times 3\) & 256 kernels in size \(1\times 3\) \\  & 1 & 512 nodes, ReLU & 512 nodes, ReLU & 512 nodes, ReLU & 512 nodes, ReLU \\  & 2 & 32 nodes, ReLU & 32 nodes, ReLU & 32 nodes, ReLU & 32 nodes, ReLU \\  & 2 & 1 node & 1 node & 1 node & 1 node \\ \hline \hline \end{tabular}
\end{table}
Table 3: CNN architectures with two convolutional layers.  
and classification [48]. Gradient boosting regression (GBR) builds a robust composite prediction model by combining multiple weak learners. Partial least squares regression (PLSR) is extensively used for the regression modeling of high-dimensional datasets [20].  
In this study, the average spectra of 204 grape leaves and their corresponding _chlorophyll a + b_ content were used to establish models using SVR, RFR, GBR, and PLSR.