A Systematic Review of Machine Learning Methods in Smart Hydroponic Farming

A Systematic Review of Machine Learning Methods in Smart Hydroponic Farming | 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 A Systematic Review of Machine Learning Methods in Smart Hydroponic Farming Joseph, O. Ukoba, Ugochi, A. Okengwu, Fubara Egbono This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6667521/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 The burgeoning global population coupled with the increasing scarcity of arable land has necessitated innovative agricultural practices. Hydroponics, a soil-less cultivation method, has emerged as a promising solution to address these challenges by offering efficient and sustainable food production. This systematic review explores the application of machine learning methods in smart hydroponic farming. The analysis reveals a growing trend in the use of machine learning techniques to address challenges such as disease detection, parameter control, and yield prediction. Common methods include decision trees, neural networks, Bayesian networks, and support vector machines. While significant progress has been made, research gaps remain in yield growth prediction and data security. Future research should focus on integrating advanced technologies like IoT, AI, robotics, blockchain, and GIS to enhance the efficiency, sustainability, and scalability of smart hydroponic farming. Full Text Additional Declarations No competing interests reported. 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-6667521","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459848071,"identity":"2fd2ca68-f3f8-49ab-95d2-1cf9aa9175c5","order_by":0,"name":"Joseph, O. 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