## 3 Selection of Basic Deep Learning Mode
### Training Method of Deep Learning Model  
Convolutional neural networks require extensive training to seek optimal hyperparameters. With different hyperparameters as variables, multiple control experiments are conducted. The design is as follows: First, three convolutional neural networks, ResNet-101, DenseNet-121, and AlexNet, are employed to build a wheat drought stress classification identification model, and the model is trained from scratch. During the training process, all data is divided into training and test sets, randomly distributed at an 8:2 ratio, where 80% of the samples are used for model training, and 20% for model testing. During model training, the learning rate decay method is set to fixed (Fixed), with a learning rate of 0.001, optimizer set to Stochastic Gradient Descent (SGD), and a batch size of 100. Firstly, the model's convergence is judged by comparing the model's loss function, and at the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Label Category**} & \multicolumn{3}{c}{**Model Prediction**} \\ \cline{2-4}  & & **0** & **1** \\ \hline \multirow{2}{*}{Truth Label} & 0 & True positive (TP) & False negative (FN) \\  & 1 & False positive (FP) & True negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 6: The confusion matrix for determining whether the classification of winter wheat is correct based on the recognition model.  
same time, by comprehensively comparing indicators such as recognition accuracy and F1 value, the basic network model with the best generalization ability on this dataset is selected. Secondly, after selecting the basic network model, a total of eight combination experiments are carried out with training methods, learning rate, and attention mechanism as variables, comprehensively comparing the model's recognition accuracy and F1 value indicators, to find the most suitable convolutional neural network model and corresponding hyperparameters  
### Training Method of Deep Learning Model  
In this section, ResNet-101, DenseNet-121, and AlexNet convolutional neural networks are used to build the wheat drought stress classification identification model, respectively. The model's generalization capability is judged by accuracy and F1 value, and the model's convergence is verified by the loss function, thereby selecting the network model with the best generalization capability on this dataset as the basic model. The experimental results of the evaluation indicators of the three deep learning models are shown in Table 7. Figure 7 displays the loss values, accuracy, and F1 values of ResNet-101, DenseNet-121, and AlexNet convolutional neural networks on the test set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Metrics** & **ResNet101** & **DenseNet121** & **AlexNet** \\ \hline Drought Identification Accuracy & 74.80\% & 87.60\% & 55.83\% \\ Drought Classification Precision & 79.10\% & 88.36\% & 63.38\% \\ Drought Identification Recall & 74.79\% & 87.59\% & 55.83\% \\ F1 value & 0.7173 & 0.8729 & 0.5326 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Basic experimental results of three network models based on test sets.  
Figure 7: Loss values, accuracy, and F1 score of different deep learning models on the test set.  
From Table 6, it can be observed that, in terms of drought identification accuracy, the DenseNet-121 model has the highest precision with an accuracy of 87.60% on the test set. ResNet-101 and AlexNet models have accuracies of 74.80% and 55.83%, respectively. Judging from the model's loss function and F1 value, the convergence speed of the DenseNet-121 model is faster than that of the ResNet-101 and AlexNet models, and the ResNet-101 model exhibits overfitting. The F1 value of the DenseNet-121 model is 0.8729, higher than the 0.7173 and 0.5326 of the ResNet-101 and AlexNet models, respectively. In a comprehensive comparison, the DenseNet-121 network model is chosen as the experimental basic network model.