IoT-based Smart Drip Irrigation Scheduling and Wireless Monitoring of Microclimate in Sweet Corn Crop under Plastic Mulching

IoT-based Smart Drip Irrigation Scheduling and Wireless Monitoring of Microclimate in Sweet Corn Crop under Plastic Mulching | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article IoT-based Smart Drip Irrigation Scheduling and Wireless Monitoring of Microclimate in Sweet Corn Crop under Plastic Mulching Vinod Kumar s, C D Singh, K V Ramana Rao, Yogesh A Rajwade, Mukesh Kumar, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4234762/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Precision irrigation with IoT-based decision-making technologies has proven effective in optimizing agricultural production irrigation. The Internet of Things (IoT) is crucial for monitoring the real-time data from sensors and automatically activating irrigation systems. This study evaluated the effectiveness of a drip irrigation system based on IoT and soil moisture sensors in a field experiment with sweet corn between 2020 and 2022. There were nine treatments with three replications: ETc-based drip irrigation (ETc 100%) and IoT-based drip irrigation scheduling with two soil moisture levels under three mulches: black plastic mulch, silver plastic mulch, and control (bare soil). IoT-based drip irrigation scheduling (100% FC) applied irrigation when soil moisture reached a lower threshold (≤ 33.1%) and ended when the field capacity was reached (≥ 43.5). With IoT-based drip irrigation scheduling (80% FC), irrigation was applied when the soil moisture content reached the threshold (≤ 33.1%) and ended when the field capacity reached 80% (≥ 34.8). Growth variables (root biomass, yield, corn length, cob weight, and water productivity) were compared for each irrigation method. Results showed that the ET-based irrigation method was easier to implement with less infrastructure and could result in lower yields than the IoT-based drip irrigation method with 100% FC. Grain and stalk yields increased by more than 12.05% and 14.97% for the IoT irrigation with 100% FC. It was found that IoT-based drip irrigation with 100% FC and 80% FC used 12.7% and 24.5% less irrigation water, respectively, and provided IoT-based drip irrigation with 100% FC, there was a 12.8% increase in marketable yield than ETc and IoT-based drip irrigation with 80% FC. The results show that the developed IoT system can potentially monitor the microclimate of plants in real time under different conditions of using plastic mulch. The IoT system is rugged and water-resistant, making it suitable for outdoor agriculture. Solar panels power the system, so there is no need for cabling and sensor nodes can be efficiently monitored. Research conducted on the IoT system shows that it can record and display environmental parameters to users via the cloud (ThingSpeak). Zea mays Smart irrigation Evapotranspiration (ET) Soil moisture sensors Mulch Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 May, 2024 Reviews received at journal 06 May, 2024 Reviews received at journal 22 Apr, 2024 Reviewers agreed at journal 19 Apr, 2024 Reviewers agreed at journal 18 Apr, 2024 Reviewers invited by journal 15 Apr, 2024 Editor assigned by journal 10 Apr, 2024 Submission checks completed at journal 09 Apr, 2024 First submitted to journal 08 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4234762","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291030956,"identity":"7a6b6e9e-02f7-4682-9d53-61653d3537ad","order_by":0,"name":"Vinod Kumar 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The Internet of Things (IoT) is crucial for monitoring the real-time data from sensors and automatically activating irrigation systems. This study evaluated the effectiveness of a drip irrigation system based on IoT and soil moisture sensors in a field experiment with sweet corn between 2020 and 2022. There were nine treatments with three replications: ETc-based drip irrigation (ETc 100%) and IoT-based drip irrigation scheduling with two soil moisture levels under three mulches: black plastic mulch, silver plastic mulch, and control (bare soil). IoT-based drip irrigation scheduling (100% FC) applied irrigation when soil moisture reached a lower threshold (\u0026le;\u0026thinsp;33.1%) and ended when the field capacity was reached (\u0026ge;\u0026thinsp;43.5). With IoT-based drip irrigation scheduling (80% FC), irrigation was applied when the soil moisture content reached the threshold (\u0026le;\u0026thinsp;33.1%) and ended when the field capacity reached 80% (\u0026ge;\u0026thinsp;34.8). Growth variables (root biomass, yield, corn length, cob weight, and water productivity) were compared for each irrigation method. Results showed that the ET-based irrigation method was easier to implement with less infrastructure and could result in lower yields than the IoT-based drip irrigation method with 100% FC. Grain and stalk yields increased by more than 12.05% and 14.97% for the IoT irrigation with 100% FC. It was found that IoT-based drip irrigation with 100% FC and 80% FC used 12.7% and 24.5% less irrigation water, respectively, and provided IoT-based drip irrigation with 100% FC, there was a 12.8% increase in marketable yield than ETc and IoT-based drip irrigation with 80% FC. The results show that the developed IoT system can potentially monitor the microclimate of plants in real time under different conditions of using plastic mulch. The IoT system is rugged and water-resistant, making it suitable for outdoor agriculture. Solar panels power the system, so there is no need for cabling and sensor nodes can be efficiently monitored. 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