Research Article

Predicting Wheat Response to Drought Using Machine Learning Algorithms  

Weichang Wu
Jiugu MolBreed SciTech Ltd., Zhuji, 311800, China
Author    Correspondence author
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
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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)


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.

Wheat; Drought response; Machine learning algorithms; Growth index; Drought disaster
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