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How can agricultural water production be promoted? a review on machine learning for irrigation
Journal of Cleaner Production ( IF 11.1 ) Pub Date : 2023-06-02 , DOI: 10.1016/j.jclepro.2023.137687
Hairong Gao, Lili Zhangzhong, Wengang Zheng, Guangfeng Chen

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 irrigation 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 improvements in irrigation efficiency, and ML is thus expected to become an important solution for the modernization 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 irrigation. 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.



中文翻译:

如何促进农业用水生产?灌溉机器学习综述

联合国粮食及农业组织(FAO)表示,灌溉技术是改善粮食安全的关键。然而,当前受限的农业水土资源限制了农业生产体系,全球粮食安全压力巨大。精确和智能灌溉技术的发展对于保持必要的农业增长率而不进一步破坏环境至关重要。机器学习算法的快速发展为提高灌溉效率提供了契机,机器学习有望成为灌溉现代化的重要解决方案。灌溉系统。这篇综述整理了所有关于灌溉 ML 的研究,并介绍了灌溉中使用的 ML 算法的类型、数据来源和 ML 的演变。在缺水诊断、需水量预测和灌溉决策方面详细描述了ML 的发现,同时阐述了文献的演变以及 ML 在灌溉领域的优缺点。以水资源的高效可持续发展为目标,提出了基于机器学习的智能灌溉模型框架,为智能灌溉技术的研究提供了基础。

更新日期:2023-06-07
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