Smart Irrigation Control Using IoT-Enabled Fuzzy Logic and ANFIS for Sustainable Water Management

Smart Irrigation Control Using IoT-Enabled Fuzzy Logic and ANFIS for Sustainable Water Management | 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 Smart Irrigation Control Using IoT-Enabled Fuzzy Logic and ANFIS for Sustainable Water Management IDOWU OLUGBENGA ADEWUMI, Victoria Bola Oyekunle, Waheed Azeez Ajani, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7399502/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Efficient irrigation management is critical for improving water use efficiency, increasing crop productivity, and ensuring long-term sustainability in agriculture. This study presents an Internet of Things (IoT)-enabled fuzzy logic irrigation control system that adaptively adjusts irrigation schedules based on real-time soil moisture, ambient temperature, and humidity data. The fuzzy inference engine, designed with expert knowledge and implemented using Mamdani-type reasoning, was benchmarked against conventional fixed-schedule irrigation and an AI threshold-based control system across multiple soil types and environmental conditions. Experimental results demonstrate that the proposed system reduced water consumption by 31.4% compared with conventional methodsand by 12.7% compared with threshold-based AI, while achieving a 22.8% increase in average crop yieldand a system reliability of 98.6%. Statistical validation using one-way ANOVA and Tukey’s HSD confirmed the significance of these improvements (p < 0.05). Beyond efficiency gains, the fuzzy logic approach proved effective in handling sensor noise, mitigating uncertainty, and providing smooth, adaptive control actions without abrupt fluctuations. Furthermore, the modular design of the system facilitates integration with adaptive neuro-fuzzy inference systems (ANFIS), enabling self-learning and continuous optimization. These findings highlight the potential of IoT-driven fuzzy systems as scalable, cost-effective solutions for precision agriculture and sustainable water resource management. Artificial Intelligence and Machine Learning Fuzzy logic control IoT smart irrigation precision agriculture ANFIS water-use efficiency sustainable farming Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7399502","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501936739,"identity":"1617abe1-1008-47e1-9fb5-cb5fd91a39d8","order_by":0,"name":"IDOWU OLUGBENGA 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This study presents an Internet of Things (IoT)-enabled fuzzy logic irrigation control system that adaptively adjusts irrigation schedules based on real-time soil moisture, ambient temperature, and humidity data. The fuzzy inference engine, designed with expert knowledge and implemented using Mamdani-type reasoning, was benchmarked against conventional fixed-schedule irrigation and an AI threshold-based control system across multiple soil types and environmental conditions. Experimental results demonstrate that the proposed system reduced water consumption by 31.4% compared with conventional methodsand by 12.7% compared with threshold-based AI, while achieving a 22.8% increase in average crop yieldand a system reliability of 98.6%. Statistical validation using one-way ANOVA and Tukey’s HSD confirmed the significance of these improvements (p \u0026lt; 0.05). Beyond efficiency gains, the fuzzy logic approach proved effective in handling sensor noise, mitigating uncertainty, and providing smooth, adaptive control actions without abrupt fluctuations. Furthermore, the modular design of the system facilitates integration with adaptive neuro-fuzzy inference systems (ANFIS), enabling self-learning and continuous optimization. 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