## 5 Conclusions  
This paper proposes a deep learning-based drought monitoring model for the growth process of winter wheat, which aims at identifying and classifying different degrees of drought stress during winter wheat's growing period. In this paper, we focus on three key growth periods in winter wheat, namely RJ, HF, and FM, for drought stress. In these periods, we collected winter wheat drought-stress images from the field conditions and established a drought image set corresponding to soil moisture monitoring data. By comparing the drought stress recognition accuracy, classification accuracy, recall rate, and F1 value of the AlexNet, ResNet-101, and DenseNet-121 network models, the DenseNet-121 network model was chosen as the base model for this purpose. Building upon the selected DenseNet-121, we developed training and optimization strategies for the model, considering improvements like the use of transfer learning, the adoption of a gradual learning rate, and the introduction of the attention mechanism method. Eight experimental groups were carried out. The results indicate that through these improvements, the model's convergence speed is accelerated and performance metrics, including recognition accuracy, are enhanced. This effectively improves the identification and classification of drought stress levels during the key growth stages of winter wheat. Notably, the test set's average recognition accuracy for Experiment 8 reaches 94.67%. A comprehensive analysis reveals that deep learning algorithms provide a more reliable and accurate method for identifying and classifying the degree of wheat drought. While advancements have been noted in the study of drought stress phenotypes, the diagnosis of crop drought stress based solely on a single phenotypic trait encounters notable challenges. Factors such as fluctuating weather conditions and variations in lighting can result in image overexposure or the loss of critical details, thereby impeding the precise identification of drought indicators. Furthermore, the prevalence of pests and diseases in agricultural settings, which can mimic the symptoms of drought stress, compounds the potential for erroneous assessments by image-based monitoring systems. To transcend these limitations, it is imperative for future research to embrace the integration of data from multiple sensors to encapsulate a holistic view of crop phenotyping. This encompasses a range of attributes--color, texture, morphology, and physiological parameters--that define the crop's phenotype. The fusion of this rich, multi-dimensional dataset with sophisticated pattern recognition algorithms paves the way for the development of more precise and non-destructive diagnostic tools for the rapid monitoring of crop drought stress. Thisintegrated approach is anticipated to markedly enhance the fidelity and dependability of monitoring efforts, thereby affording robust decision support for agricultural practices. This research was a collaborative effort with each author contributing significantly to various aspects of the study. [PERSON] was instrumental in developing the Methodology, ensuring the study's approach was both robust and relevant. [PERSON] took the lead in preparing the Writing--Original Draft, meticulously crafting the initial manuscript that laid the foundation for our work. [PERSON] provided essential Supervision throughout the project, guiding the research team with his expertise and ensuring the study met the highest academic standards. [PERSON] contributed by creating insightful Visualizations, effectively communicating complex data and findings in a clear and accessible manner. Each author has played a pivotal role in the research process, and all have read and agreed to the final published version of the manuscript. The authorship is attributed only to those who have made substantial contributions to the work as reported. All authors have read and agreed to the published version of the manuscript.