Plant Gene and Trait 2024, Vol.15, No.1, 1-7 http://genbreedpublisher.com/index.php/pgt 3 Drought response of wheat also involves the regulation of expression of many stress-related genes. These genes may encode protective proteins, such as proline aminolyase and antioxidant enzymes, to alleviate oxidative damage caused by stress. In addition, the regulation of several transcription factors and signal transduction elements is also key, which can initiate or inhibit gene expression in specific pathways and regulate stress response networks within cells. 2.3 Restrictions and limitations of traditional research methods The traditional research methods often have some restrictions and limitations in analyzing the mechanism of wheat response to drought. For the complex biological processes related to drought stress, traditional research methods are difficult to analyze comprehensively and efficiently. Traditional biological and physiological research is limited to the study of specific biological processes or biomolecules, which leads to a potentially incomplete understanding of the overall mechanisms of drought stress response (Mwadzingeni et al., 2016). Because drought stress involves complex interactions at the molecular, cellular, and tissue levels, traditional biological approaches alone may not provide a full insight into the complexity of these interactions. Traditional experimental operations are also limited by time and space. Drought is a gradual process, and it may take a long time to monitor the physiological and molecular changes of wheat under different drought degrees. In addition, due to the limitation of experimental environment, it is difficult for traditional methods to completely simulate the complex natural drought environment. Traditional methods may also have the problem of insufficient detection sensitivity. Some molecular changes or interactions may require more sensitive instruments or techniques to accurately capture, and traditional methods may not be able to meet this need, resulting in some subtle but critical molecular changes being overlooked or masked. 3 Machine Learning Model 3.1 Selection and application of typical machine learning models In studies exploring wheat's response to drought, typical machine learning models are widely used to predict and explain its response mechanism, and selecting an appropriate machine learning model is crucial to understanding wheat's drought response mechanism. In addition to wheat, machine learning models are also applied to other plants, taking corn as an example. In the field of genomics and epigenomics, machine learning models can help analyze the genomic data of corn and identify gene functions, regulatory networks and biological pathways. The application of these models provides important clues for gene editing and breeding of corn. Machine learning also plays a key role in the prediction and control of maize diseases. By analyzing disease data, the model can quickly identify disease types and provide corresponding prevention and control suggestions to help farmers prevent and control diseases in time. For the analysis of corn growth and ecological environment, machine learning models can also predict corn yield and adaptability according to various factors such as climate, soil and growth conditions, providing an important reference for agricultural production. In corn crop management and precision agriculture, machine learning techniques can optimize agricultural decisions based on real-time data, such as providing guidance on water use and fertilization, to improve corn growth quality and agricultural yield. Common machine learning models include decision trees, support vector machines, random forests, neural networks, regression models, etc. (Cai et al., 2021). Decision tree model has attracted much attention because it is easy to understand and interpret. It gradually generates decision rules by branch selection of data set. Support vector machines (SVMS) classify and regression data by constructing hyperplanes and are suitable for complex and nonlinear data sets. Random forest is an integrated model based on multiple decision trees, which can efficiently process a large number of features and data sets. Neural networks mimic the connection patterns of human brain neurons and are suitable for processing complex and large-scale data, but require more data volume and computational resources. Regression models are often used to predict the response of continuous variables such as wheat growth or yield. In the application of these models, it is necessary to consider the selection and preprocessing of data features, the optimization of model parameters, the problems of overfitting and underfitting, and the interpretability of models. In addition, for wheat drought response prediction, it is usually necessary to integrate multiple machine learning models to improve the accuracy and robustness of the prediction.
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