## 4 Model Optimization Strategies and Result Analysis
### Experimental Results Validation
A comparative assessment of AUC scores across various experiments highlights the effectiveness of the improvements made in this paper. Notably, in Experiments 1 and 8, which focus on the RJ-MD classification scheme, we observed a remarkable 15% increase in AUC. This increase signifies a substantial enhancement in the model's ability to differentiate between positive and negative cases, reflecting an overall improvement in diagnostic accuracy. Furthermore, upon examining the performance of various improved methods, several stood out. The transfer learning approach, which involved fine-tuning the training of all layers, the model employing an asymptotic learning rate strategy, and the one integrated with an attention mechanism all performed exceptionally well in terms of AUC and specificity, suggesting that these enhancements are crucial for optimizing model performance. To provide a comprehensive understanding, this paper conducts an in-depth analysis of the ROC curves for Experiments 1, 4, and 8, as shown in Figure 12. We analyzed the trade-off between the true positive rate and the false positive rate at various threshold settings. This detailed examination allows us to evaluate the models' performance across a spectrum of classification decisions, offering insights into their robustness and reliability in different scenarios. Figure 12: Cont. In conclusion, the enhanced model outperforms the pre-improved model across all evaluated metrics. The improvements are not only statistically significant but also practically meaningful, providing a solid foundation for further research and application in the field of winter wheat drought stress monitoring. Figure 12: ROC curves for different experimental results. In this section, we explored the impacts of transfer learning methods and learning rates on model performance and investigated the effects of the attention mechanism on model training results. Experimental results show that fine-tuning all layers rather than just the final layer can further enhance model performance; models using a gradual learning rate method slightly outperform those with a fixed learning rate, indicating that the gradual learning rate method effectively manages and adjusts the learning rate of the model. Introducing an attention mechanism to models slightly improves recognition accuracy over models without it. This is because the attention mechanism enhances the model's weighted importance of wheat features and focuses on key information, improving the model's perception of importance across different features, time steps, and spatial positions. In this section, by leveraging the feature extraction capabilities of pre-trained models, dynamically adjusting learning rates, and enhancing attention to vital information and its importance, we aim to elevate the detection performance, accuracy, and generalization ability of the model. These strategies, applied across diverse tasks and network structures, can significantly boost the generalization capabilities of deep learning models. The integrated application of these methods in this section can improve the training results and generalization ability of the model, speed up model convergence, and enhance performance metrics such as recognition accuracy. Specifically, the test set's average recognition accuracy for Experiment 8 reached 94.67%, demonstrating promising potential in recognizing drought levels during critical wheat growth stages.