基于边缘计算、机器学习与物联网的智能灌溉系统提升农业水资源管理(土耳其Mustafa & Kutucu,Frontiers 2026-07-28接受)
论文结合边缘计算、机器学习与物联网,提出智能灌溉系统,方法新颖,数据详实,对农业水资源管理有实际价值,发表于Frontiers期刊,权威性高,时效性好。
Mustafa MS(卡拉比克大学计算机工程系)与Kutucu H(土耳其)在Frontiers in Sustainable Food Systems发表的原创研究"Enhancing Water Management with Intelligent Irrigation Systems Using Edge Computing, Machine Learning and IoT"于2026-07-28接受。针对农业占全球淡水取用量约70%、传统灌溉水耗大、效率低的现实,研究构建将物联网、机器学习与边缘计算融合的智能灌溉系统:物联网传感器和边缘设备实时采集环境数据并估算灌溉需求,机器学习模型与土壤监测单元(SMU)和水管理单元(WMU)协同,在边缘本地部署实现可扩展、实时、高效灌溉管理。研究比较决策树(DT)、随机森林(RF)、支持向量机(SVM)、K近邻(KNN)、随机梯度下降(SGD)和多层感知机(MLP)六种监督学习模型,其中决策树和随机森林分类准确率达93%、R²回归得分0.98,显著优于SVM(准确率58%、R²=0.18),为不同农业场景下水资源高效管理提供可复用方案。原文:Enhancing Water Management with Intelligent Irrigation Systems Using Edge Computing, Machine Learning and IoT
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- Crop Biology and Sustainability
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ORIGINAL RESEARCH article
Front. Sustain. Food Syst.
Sec. Water-Smart Food Production
Volume 10 - 2026 | doi: 10.3389/fsufs.2026.1827416
Published in
Frontiers in Sustainable Food Systems * #### Water-Smart Food Production * 4.1 impact factor * 6 citescorePart of a Research Topic Advanced technologies for water management: targeting sustainable agriculture Submission open * 19k views * 11 articles
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Outline
ORIGINAL RESEARCH article
Front. Sustain. Food Syst.
Sec. Water-Smart Food Production
Enhancing Water Management with Intelligent Irrigation Systems Using Edge Computing, Machine Learning and IoT
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- Karabuk Universitesi Bilgisayar Muhendislik Bolumu, Karabük, Türkiye
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- Karabuk Universitesi Yazilim Muhendisligi Bolumu, Karabük, Türkiye
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Abstract
Agriculture is respons