How can agricultural water production be promoted? a review on machine learning for irrigation

文献类型： 外文期刊

第一作者： Gao, Hairong

作者： Gao, Hairong;Zhangzhong, Lili;Zheng, Wengang;Gao, Hairong;Zhangzhong, Lili;Zheng, Wengang;Chen, Guangfeng

作者机构：

关键词： Machine learning; Water -scarcity diagnosis; Water -demand prediction; Irrigation decision -making; Model framework

期刊名称：JOURNAL OF CLEANER PRODUCTION （ 影响因子：11.1； 五年影响因子：11.0 ）

ISSN： 0959-6526

年卷期： 2023 年 414 卷

页码：

收录情况： SCI

摘要： The Food and Agriculture Organization (FAO) indicated that irrigation technology is the key to improving food security. However, the current restricted agricultural water and land resources limit the agricultural production system, and the pressure on global food security is enormous. The development of precise and intelligent irri-gation technology is crucial for maintaining the necessary agricultural growth rates without further damage to the environment. The rapid development of machine learning (ML) algorithms provides opportunities for im-provements in irrigation efficiency, and ML is thus expected to become an important solution for the modern-ization of irrigation systems. This review collates all the research on ML in irrigation and presents the types of ML algorithms used in irrigation, the sources of data, and the evolution of ML. The findings on ML are described in detail in terms of water scarcity diagnosis, water demand prediction, and irrigation decision-making while elaborating on how the literature has evolved and the advantages and disadvantages of ML in the field of irri-gation. Aiming for efficient and sustainable development of water resources, we propose an intelligent irrigation model framework based on ML, which provides the basis for the research on intelligent irrigation technology.

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