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                <publisherName>ZIBELINE INTERNATIONAL PUBLISHING</publisherName>
                <title type="subject" xml:lang="en" sort="Big Data In Agriculture">Big Data In Agriculture</title>
                <abbrev_title>Big.data.Agr</abbrev_title>
                <issn type="online">2682-7786</issn>
            </publisherInfo>
            <titleGroup>
                <title type="title">IOT-BASED SMART IRRIGATION AND ENVIRONMENTAL MONITORING SYSTEM USING CLOUD PLATFORM AND MOBILE APP INTEGRATION</title>
            </titleGroup>
            <copyright ownership="publisher">Copyright © 2025 Zibeline International Publishing</copyright>
            <doi origin="zibeline international publishing" registered="yes">http://doi.org/10.26480/bda.02.2026.66-71</doi>
            
            <eventGroup>
                <event type="publication_date" date="20-05-2026" />
            </eventGroup> 
            
            <creators>    
                <creator xml:id="DA" creatorRole="editor">
                    <personName>
                        <editorNames>Danish Akram</editorNames>
                    </personName>
                </creator>
                <creator xml:id="MAM" creatorRole="editor">
                    <personName>
                        <editorNames>Muhammad Ahmad Mehmood</editorNames>
                    </personName>
                </creator>
                <creator xml:id="RKQ" creatorRole="editor">
                    <personName>
                        <editorNames>Rana Khuzaima Qudoos</editorNames>
                    </personName>
                </creator>
                <creator xml:id="HS" creatorRole="editor">
                    <personName>
                        <editorNames>Hammad Shahab</editorNames>
                    </personName>
                </creator>
				<creator xml:id="SH" creatorRole="editor">
                    <personName>
                        <editorNames>Shahzad Hussain</editorNames>
                    </personName>
                </creator>
				<creator xml:id="MMW" creatorRole="editor">
                    <personName>
                        <editorNames>Muhammad Mohsin Waqas</editorNames>
                    </personName>
					</creator>
            </creators>
            
        </publicationMeta>
        <citation_keywords>
            <keyword>Smart Irrigation, ESP32, Dual-Mode Control, loT Agriculture, Precision Farming, Cloud Monitoring, Thing Speak</keyword>
        </citation_keywords>
        <citation_pdfformat>
            <pdf_url>https://bigdatainagriculture.com/paper/issue12026/2bda2026-66-71.pdf</pdf_url>
        </citation_pdfformat>
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            <xml_url>https://bigdatainagriculture.com/xml/issue12026/2bda2026-66-71.xml</xml_url>
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        <citation_volume>
            <volume>8</volume>
        </citation_volume>
        <citation_issue>
            <issue>1</issue>
        </citation_issue>
        <citation_pages>
            <pages>66-71</pages>
        </citation_pages>
        
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            <fulltext_html>https://bigdatainagriculture.com/bda-02-2026-66-71/</fulltext_html>
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        <abstractGroup>
            <abstract type="main" xml:lang="en">
                <title type="main">Summary</title>
                <p>Efficient water management and intelligent crop monitoring are critical challenges in modern precision agriculture. Traditional irrigation systems lack adaptability, remote accessibility, and real-time environmental monitoring. This research presents a hybrid dual-mode loT-based multi-zone smart irrigation system using ESP32 controller, integrating automatic moisture-based control and manual mobile app based operation. The system monitors three independent agricultural area using soil moisture sensors and controls a centralized irrigation pump via relay-based switching. Environmental parameters including temperature, humidity, and CO levels are simultaneously recorded. The online platform supports operational modes: automatic irrigation based on crop-specific thresholds and manual override through mobile app based control. Real-time sensors data and pump status are transmitted to online Thing Speak cloud for monitoring and historical data analytics. Experimental evaluation demonstrates reliable multi-zone irrigation management, improved water efficiency, and real-time remote supervision capability of the proposed. The proposed system provides a scalable and cost-effective solution for intelligent agriculture management applications.</p>
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