Plant Gene and Trait 2024, Vol.15, No.1, 1-7 http://genbreedpublisher.com/index.php/pgt 6 large-scale data and pattern recognition, and their efficient data processing capabilities enable more accurate prediction and analysis of wheat responses under different drought conditions (Feng et al., 2019). However, machine learning models also have certain limitations, such as high dependence on data quality and label accuracy, and relatively weak interpretability of their results. In practical research, the combination of traditional methods and machine learning methods may be a more ideal path. The complementary approach of traditional methods, which drill down into physiological processes, and machine learning methods, which can process large-scale data more quickly and accurately, can help to better understand wheat's response to drought. This comprehensive application can make up for the limitations of a single method, and provide more accurate and efficient strategies and guidance for agricultural production. In the future, with the continuous development of technology and the continuous optimization of methods, the combination of machine learning and traditional research methods may become an important direction of wheat drought response research. 4.4 Model improvement suggestion In the machine learning model for predicting wheat response to drought, continuous improvement and optimization of the model is an important step to improve the accuracy and practicability of the prediction. The feature selection of the model is one of the keys. Through in-depth understanding of the physiological and molecular response mechanism of wheat to drought, more representative features can be extracted to enhance the accurate prediction ability of the model to drought response. Suitable feature selection can reduce the complexity of the model and improve the generalization ability of the model. The optimization of the model needs to consider the algorithm parameters and model architecture. For wheat drought response prediction, different machine learning algorithms can be explored and their parameters adjusted, such as support vector machines, decision trees, neural networks, etc., to find a more suitable model for the problem. At the same time, adjusting the hyperparameters and network structure of the model, such as increasing the number of layers and adjusting the learning rate, can help improve the model performance (Sundararajan et al., 2021). Data quality and quantity are also critical to model improvement. Ensuring the accuracy and completeness of the data, while collecting more and more comprehensive sample data, can help the model to better capture the complex response relationship of wheat to drought and improve the prediction accuracy. Model improvement also requires continuous verification and evaluation. The stability and generalization ability of the model are verified by cross-validation, maintaining validation set and other methods, so as to determine whether the model improvement is effective. 5 Conclusion and Prospect By reviewing a large number of previous studies, we found several important conclusions. Machine learning models show remarkable potential in analyzing wheat's response to drought, and can accurately predict wheat growth and yield under drought conditions. Secondly, the study shows that machine learning algorithms can use multi-source data, such as soil properties, meteorological data, and remote sensing information, to provide a more comprehensive perspective for predicting wheat drought response. Most importantly, these models significantly improve the forecasting accuracy and efficiency compared with traditional methods, providing more forward-looking and accurate decision support for wheat agricultural production. In the future, researchers can focus their research on several aspects. First, we need to further optimize and improve the machine learning algorithm to improve the accuracy and stability of the model in predicting wheat drought response. And explore the method of multi-model fusion, combining the advantages of different algorithms to build more powerful prediction models. In addition, the improvement of data quality and usability is also a focus of future attention, including in-depth analysis of data quality and the use of more laboratory and field validation data to ensure the robustness and adaptability of the model.
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