{"title":"Smart irrigation systems in agriculture: An overview","authors":"Vikas Sharma , Gurleen Kaur , Sreethu S. , Vandna Chhabra , Rajeev Kashyap","doi":"10.1016/j.compag.2025.111008","DOIUrl":null,"url":null,"abstract":"<div><div>Smart irrigation systems represent a transformative solution to the pressing challenges of water scarcity, climate variability, and the demand for sustainable agricultural intensification. By integrating advanced technologies such as the Internet of Things (IoT), Wireless Sensor Networks (WSNs), cloud computing, and Artificial Intelligence (AI), these systems enable real-time, data-driven monitoring and control of irrigation practices. This review provides a comprehensive overview of the architecture, core technologies, and communication protocols that support smart irrigation, with a specific emphasis on their role in enhancing crop productivity, improving water use efficiency, and fostering climate-resilient agricultural systems. The integration of AI and Machine Learning (ML) models in irrigation scheduling is critically examined, highlighting commonly used algorithms, their applications, accuracy, and associated limitations. Furthermore, the review discusses key practical challenges, including the selection criteria and limitations of various technologies, particularly in the context of smallholder farming systems. Through recent innovations and case studies, this work underscores the potential of smart irrigation systems to revolutionize water management in agriculture.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111008"},"PeriodicalIF":8.9000,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169925011147","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Smart irrigation systems represent a transformative solution to the pressing challenges of water scarcity, climate variability, and the demand for sustainable agricultural intensification. By integrating advanced technologies such as the Internet of Things (IoT), Wireless Sensor Networks (WSNs), cloud computing, and Artificial Intelligence (AI), these systems enable real-time, data-driven monitoring and control of irrigation practices. This review provides a comprehensive overview of the architecture, core technologies, and communication protocols that support smart irrigation, with a specific emphasis on their role in enhancing crop productivity, improving water use efficiency, and fostering climate-resilient agricultural systems. The integration of AI and Machine Learning (ML) models in irrigation scheduling is critically examined, highlighting commonly used algorithms, their applications, accuracy, and associated limitations. Furthermore, the review discusses key practical challenges, including the selection criteria and limitations of various technologies, particularly in the context of smallholder farming systems. Through recent innovations and case studies, this work underscores the potential of smart irrigation systems to revolutionize water management in agriculture.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.