Integrating Big Data and Machine Learning to Support Smart Village Decisions for Agricultural Productivity Improvement

Integrating Big Data and Machine Learning to Support Smart Village Decisions for Agricultural Productivity Improvement | 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 Integrating Big Data and Machine Learning to Support Smart Village Decisions for Agricultural Productivity Improvement Sukriadi Sukriadi, Andi Adawiah, Andi Afdal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7726774/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The purpose of this study develops and analyzes an integrated framework that combines Big Data analytics and Machine Learning (ML) techniques to enhance agricultural productivity within the Smart Village ecosystem. In addition to conducting a bibliometric and systematic review of peer-reviewed literature, the research proposes a conceptual model illustrating how data-driven technologies can strengthen rural decision support systems. The proposed framework is validated conceptually through synthesis of prior empirical findings and structured analysis using Scopus-indexed publications from 2019–2024. Results reveal that the integration of Big Data and ML significantly improves prediction accuracy, enables early pest detection, optimizes irrigation management, and supports sustainable productivity among smallholder farmers. The study contributes to the body of knowledge by offering a comprehensive synthesis of technological applications in rural contexts, while acknowledging limitations related to data availability, infrastructure constraints, and limited adoption among digitally marginalized communities. In conclusion, integrating Big Data and ML provides transformative potential for rural agriculture and smart village initiatives, though success depends on inclusive frameworks, affordable solutions, and supportive policy environments. Future research should focus on developing scalable, context-sensitive ML models, advancing federated learning for data security, and strengthening capacity building to ensure equitable adoption across diverse rural communities. Big Data Machine Learning Smart Village Precision Agriculture Rural Development Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Nov, 2025 Reviewers agreed at journal 21 Nov, 2025 Reviews received at journal 18 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviewers agreed at journal 31 Oct, 2025 Reviewers invited by journal 31 Oct, 2025 Editor assigned by journal 16 Oct, 2025 Submission checks completed at journal 16 Oct, 2025 First submitted to journal 16 Oct, 2025 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-7726774","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542049100,"identity":"35549bd5-d0de-48f3-9d93-0a3377a73459","order_by":0,"name":"Sukriadi 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