Plant Gene and Trait 2024, Vol.15, No.1, 1-7 http://genbreedpublisher.com/index.php/pgt 1 Research Article Open Access Predicting Wheat Response to Drought Using Machine Learning Algorithms Weichang Wu Jiugu MolBreed SciTech Ltd., Zhuji, 311800, Zhejiang, China Corresponding email: 3397575099@qq.com Plant Gene and Trait, 2024, Vol.15, No.1 doi: 10.5376/pgt.2024.15.0001 Received: 10 Dec., 2023 Accepted: 25 Jan., 2024 Published: 15 Feb., 2024 Copyright © 2024 Wu, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Wu W.C., 2024, Predicting wheat response to drought using machine learning algorithms, Plant Gene and Trait, 15(1): 1-7 (doi: 10.5376/pgt.2024.15.0001) Abstract 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. Keywords Wheat; Drought response; Machine learning algorithms; Growth index; Drought disaster 1 Introduction As one of the world's most important food crops, wheat plays an indispensable role in maintaining food security and safeguarding human survival, and its high-yield and high-quality production is essential to meet the needs of the world's growing population. However, wheat production faces challenges from a variety of environmental pressures, the most significant of which is drought, which not only directly affects the growth and development of wheat, but also leads to a sharp decline in production, and triggers a global food crisis, which in turn threatens global food security (Zhang et al., 2023). In recent years, with the rapid development and wide application of machine learning technology, the agricultural field has gradually begun to use these advanced algorithms and models to solve many problems in wheat production, especially in predicting the response of wheat crops to drought. Machine learning models have shown great potential (Ding et al., 2020, IT Manager World, 23(6): 188-189). However, despite some progress in research, there are still some challenges and problems in practical applications, such as limitations in data acquisition, bottlenecks in model accuracy, and so on. Therefore, further research is needed on how to better use machine learning algorithms to predict wheat's response to drought. The purpose of this study is to systematically investigate the prediction of wheat response to drought, and to analyze its application in agricultural research from the perspective of machine learning model. By summarizing the existing research results, evaluating the advantages and limitations of machine learning models in predicting the effects of drought on wheat yield and quality, and looking forward to future research, the aim is to provide theoretical support and guidance for improving wheat drought resistance and ensuring food security. 2 Response Mechanism of Wheat to Drought 2.1 Changes in physiological processes Wheat showed a variety of physiological process changes in arid environment to adapt to water restriction stress. In the face of drought stress, the physiological processes of wheat plants have been adjusted and changed in many aspects. In response to water stress, wheat adopted a series of water regulation strategies. Plants reduce transpiration by regulating stomatal opening and closing to reduce water loss (Zhang et al., 2019). At the same time, root morphology and structure change to enhance water absorption and utilization, including the deep penetration of roots into the soil and the increase of capillary roots (Figure 1).
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