Crop Recommendation in Precision Agriculture Using Machine Learning Techniques

Crop Recommendation in Precision Agriculture Using Machine Learning Techniques | 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 Article Crop Recommendation in Precision Agriculture Using Machine Learning Techniques Nivedita Adhik Patil, Sandip Mane This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3834326/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 Analysts are increasingly interested in planning and organizing land activities near the shore, a trend that has emerged in recent years. This interest is driven by various factors, particularly the increasing focus on agricultural land and research on soil health. Soil strength plays a crucial role in enhancing crop yields, making it a significant area of research focus in local regions. The research discussed in this work delves into the study of water flow, examining its potential benefits and the problems it may pose. The primary emphasis is on scientifically investigating various advanced and efficient clustering systems and methods. The goal is to understand how these methods contribute to improving the accuracy of classification. To enhance classification accuracy, it is vital to make effective use of remotely sensed data features and choose the most suitable classifier. In our project, we aim to predict crops and weather conditions like temperature, humidity, pH, and rainfall based on soil attributes such as nitrogen, phosphorus, potassium, also the season and region. We have employed the Random Forest algorithm, selecting the configuration that yields the highest prediction accuracy. Ultimately, our efforts have resulted in achieving an impressive 93.7% accuracy using the Random Forest algorithm. Biological sciences/Evolution Physical sciences/Engineering Physical sciences/Mathematics and computing Random Forest Crop Prediction System Recommendation System Support Vector Machine Bagging Boosting 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. 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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-3834326","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":268234170,"identity":"d450660b-b695-4012-81f1-2f3407d3a870","order_by":0,"name":"Nivedita Adhik Patil","email":"data:image/png;base64,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","orcid":"","institution":"Rajarambapu Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Nivedita","middleName":"Adhik","lastName":"Patil","suffix":""},{"id":268234171,"identity":"3573d179-5c18-4ceb-91b8-0b09596ba741","order_by":1,"name":"Sandip Mane","email":"","orcid":"","institution":"Rajarambapu Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sandip","middleName":"","lastName":"Mane","suffix":""}],"badges":[],"createdAt":"2024-01-04 10:44:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3834326/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3834326/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54956292,"identity":"6bd59ad0-e549-4ab5-9766-3c39fdd1c6e8","added_by":"auto","created_at":"2024-04-19 07:20:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":626475,"visible":true,"origin":"","legend":"","description":"","filename":"JournalPaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3834326/v1_covered_454ab48c-599d-4e18-9a7c-ed3f27d50da5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCrop Recommendation in Precision Agriculture Using Machine Learning Techniques\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"Random Forest, Crop Prediction System Recommendation System, Support Vector Machine, Bagging, Boosting","lastPublishedDoi":"10.21203/rs.3.rs-3834326/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3834326/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnalysts are increasingly interested in planning and organizing land activities near the shore, a trend that has emerged in recent years. 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