Predicting Wheat Response to Drought Using Machine Learning Algorithms

With the intensification of global climate change, drought poses a serious threat to agricultural output, so it is essential to find accurate forecasting methods. Machine learning algorithms such as support vector machines, neural networks and random forests have been widely used in modeling and forecasting wheat drought response. By analyzing multidimensional data during plant growth, these algorithms are able to identify key growth indicators and drought response factors, providing a powerful tool to improve the cultivation and management of drought resistance in wheat. This review summarizes the research progress in using machine learning algorithms to predict wheat crop response to drought, highlights the potential of machine learning in predicting wheat drought response, and suggests directions for future research to further improve the prediction accuracy and applicability of wheat drought resistance.