## 4 Model Optimization Strategies and Result Analysis
### Experimental Results Validation  
The confusion matrix is one of the factors used to evaluate the model. It is a heatmap in matrix form, where rows represent the actual category labels and columns represent the predictions. The confusion matrices for the results of Experiment 1, Experiment 4, and Experiment 8 are derived through calculations, as shown in Figure 11. The horizontal and vertical coordinates in the figure are composed of abbreviations with prefixes and suffixes, where the prefix represents the growth cycle of winter wheat, such as rising-joining stage (RJ), heading-flowering stage (HF), and flowering-maturity stage (FM). Stuffxes represent drought degrees, such as optimum moisture (OM), light drought (LD), moderate drought (MD), severe drought (SD), and extreme drought (ED). Analyzing Figure 9, we can discern the following: 1 In the three different growth stages of wheat, certain stages are mistakenly identified as other stages. For instance, the \"RJ\" stage is sometimes wrongly identified as the \"HF\" stage. This is because the growth of wheat is a continuous process. As it transitions from the \"RJ\" stage to the \"HF\" stage, features like stem, leaves, and texture do not undergo significant changes in comparison to the \"HF\" stage. 2 Adjacent drought levels within the same growth cycle are also easily confused. For instance, the appropriate state might be mistakenly determined as mild drought, mild drought may be wrongly classified as either appropriate or moderate drought, moderate drought might be mistakenly identified as mild or severe drought, and severe drought may be mistakenly identified as extreme drought. This is because drought stress is a continuous, dynamic accumulation process where the phenotypic differences of wheat plants under adjacent drought levels are minimal with unclear boundaries. 3 With continuous model improvements, misjudgments are alleviated. Especially evident from the confusion matrix of Experiment 8, misjudgments are limited to the \"RJ\" stage. This is due to the minimal physiological and morphological phenotypic differences of wheat under drought stress during this stage, leading to potential misidentification. Therefore, by utilizing transfer learning, gradual learning rate adjustments, and the introduction of the attention mechanism, this section aims to enhance the drought identification accuracy of winter wheat, thereby refining the ability to determine drought stress during the wheat growth process. Figure 10: Control chart of experimental results with and without the introduction of an attention mechanism. The Receiver Operating Characteristic (ROC) is one of the factors used to evaluate the model. The X-axis of the horizontal coordinate of the ROC curve is the False Positive Rate (FPR), also known as the False Diagnosis Rate (FDR). As shown in Equation (5), the closer the X-axis is to zero, the higher the accuracy; the vertical coordinate Y-axis is the True Positive Rate, TPR, also known as the sensitivity, and as shown in Equation (6), a larger Y-axis represents a higher accuracy. AUC (Area under Curve, AUC) denotes the area under the ROC curve, which is mainly used to measure the model generalization performance, i.e., how well the classification is conducted. It indicates the probability that positive examples are ranked in front of negative examples. Generally in classification models, the prediction results are expressed in the form of probability. \[\text{FPR}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{6}\]  
Figure 11: Confusion matrix for different experimental results. \[\text{TPR}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{7}\]  
This paper utilizes the AUC metric to reveal significant improvements in the false positive rate for the enhanced model compared to its predecessor. The enhanced model demonstrates a substantial reduction in false positives without compromising sensitivity, indicating a marked enhancement in specificity.