Towards Data Science in Agriculture with Big Data Management

Towards Data Science in Agriculture with Big Data 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Research Article Towards Data Science in Agriculture with Big Data Management Purnima Gandhi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4766405/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 agriculture data is multivariate, impulsive, and complex; this data must be analyzed and interpreted accurately to address the challenges related to agro-advisory systems, recommendation systems, smart agriculture, information systems, etc. The complex characteristics of agriculture data have made data management more crucial and challenging. Big data management is a very important task to perform to develop data science applications. Data science in the agriculture domain aids in disseminating valuable information to the end users in consumable formats. The goal of big data management is to offer batch, interactive, and iterative computations on a wide range of data problems with low latency and high efficiency. Implementation of data repositories can be a transformative solution not only in terms of data management but also in terms of the business process. The paper proposes an architecture for developing a big data repository for the agriculture domain as a rudimentary step in developing data science applications. The architecture is built and implemented using two levels of data abstraction. As a proof of concept, data are depicted using the web-based REST interface and dashboard. Data science applications such as weather, land usage, market price analytics, and crop price prediction are developed using collected data, as a proof of concept. Agricultural Engineering Computer Architecture and Engineering Information Retrieval and Management Big data Dark data data management data pipeline Farm Management System 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. 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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-4766405","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":329004795,"identity":"590454ab-6f5a-4fba-8d6a-7eef64eec0aa","order_by":0,"name":"Purnima Gandhi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYLCCBDDJwyDxAUixsROrhQcIJWeAtDATaxNIizQPiEVIi7lE7rMHD3Ps5O2lew/etvm1TZ6PmYHxw8cc3FosZ6SbGyRuSzbskTmXbJ3bd9uwjZmBWXLmNtxaDG6ksUkkbmNm7JHIMZPO7bnNCNTCxsxLWEu9PViLZc9te2K1HE4Ea2H4cTuRoBbLnmcgLceTe+6cMbbsbbid3MbM2IzXL+bsaWySP7dV27bP7jG88ePPbdv57c0HP3zE5zA4SwKIGdtALMYG3OoxtDD8wat4FIyCUTAKRigAAFn0Sy03SHzgAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6160-4607","institution":"Nirma University","correspondingAuthor":true,"prefix":"","firstName":"Purnima","middleName":"","lastName":"Gandhi","suffix":""}],"badges":[],"createdAt":"2024-07-19 05:55:58","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-4766405/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4766405/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60781266,"identity":"2ea9f347-f779-46c5-b6cb-bd6135584e3e","added_by":"auto","created_at":"2024-07-22 02:59:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":557950,"visible":true,"origin":"","legend":"","description":"","filename":"IJSAS2401641.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4766405/v1_covered_748a5755-3a2a-4a45-aeae-61a924a61c98.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTowards Data Science in Agriculture with Big Data Management\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Nirma University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Big data, Dark data, data management, data pipeline, Farm Management System","lastPublishedDoi":"10.21203/rs.3.rs-4766405/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4766405/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe agriculture data is multivariate, impulsive, and complex; this data must be analyzed and interpreted accurately to address the challenges related to agro-advisory systems, recommendation systems, smart agriculture, information systems, etc. The complex characteristics of agriculture data have made data management more crucial and challenging. 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