AgroAdvisor: Crop Yield Prediction, Crop and Fertilizer Recommendation System using Random Forest with Gradient Boosting and DeepFM for Precise Agriculture

AgroAdvisor: Crop Yield Prediction, Crop and Fertilizer Recommendation System using Random Forest with Gradient Boosting and DeepFM for Precise Agriculture | 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 AgroAdvisor: Crop Yield Prediction, Crop and Fertilizer Recommendation System using Random Forest with Gradient Boosting and DeepFM for Precise Agriculture Ashima Kukkar, Rajni Mohana, Aman Sharma, Saurav Mallik, Mohd Asif Shah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4099720/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 Crop yield prediction plays a very important role in productivity growth. Prediction of the crop yield in particular area helps the farmer to choose the right crop to be grown in the land. With crop yield prediction crop recommendation boosts up the productivity of crop. Recommending the correct type of crop in particular land on the factors of soil pH, rainfall, temperature, humidity etc. helps the farmer to choose specific and most suitable crop. With recommendation and yield prediction of crop, fertilizer recommendation is also necessary for more productivity and yield. It is necessary to use suitable fertilizers on optimal timing for the growth of crops. Therefore, in this paper, we have attempted to address these issues by proposing three model systems that will efficiently manage crop production. In this paper, we designed an integrated system named as AgroAdvisor using the hybrid proposed technique such as Random Forest with Extreme Gradient Boosting (RFXGB) and Deep Factorization Machine (DeepFM). RFGB is applied for processing the features, which improves the DeepFM ability to handle the dense numerical features and increase the prediction performance. The result of RFXGB-DeepFM is compared with classical machine learning and deep learning techniques by using recall, F-value, precision and accuracy parameters. The results show that the proposed RFGB-DeepFM technique gives better accuracy than the classical techniques. The impact of RFGXB on existing techniques is also analyzed using Friedman and post hoc statistical testing and results show that in most cases RFGXB enhanced the performance. Biological sciences/Computational biology and bioinformatics Biological sciences/Plant sciences Crop Yield Prediction Crop Recommendation System Fertilizer Recommendation System Integrated System Deep Learning Algorithms Machine Learning Algorithms DeepFM Random Forest 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-4099720","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":287396469,"identity":"29721d65-24ac-4e53-a673-22dbc319d247","order_by":0,"name":"Ashima Kukkar","email":"","orcid":"","institution":"Chitkara University Institute of Engineering and Technology, Chitkara University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ashima","middleName":"","lastName":"Kukkar","suffix":""},{"id":287396470,"identity":"76e73fc4-b50c-4a90-a1bb-201588e5bc9d","order_by":1,"name":"Rajni Mohana","email":"","orcid":"","institution":"Jaypee University of Information and Technology Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rajni","middleName":"","lastName":"Mohana","suffix":""},{"id":287396472,"identity":"6b5a6ee2-66df-403a-9687-06c86908db72","order_by":2,"name":"Aman Sharma","email":"","orcid":"","institution":"Jaypee University of Information and Technology Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Aman","middleName":"","lastName":"Sharma","suffix":""},{"id":287396474,"identity":"bdde4f00-88e8-4ba0-a618-b8d9b745c108","order_by":3,"name":"Saurav Mallik","email":"","orcid":"","institution":"Harvard T H Chan School of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Saurav","middleName":"","lastName":"Mallik","suffix":""},{"id":287396476,"identity":"e7879bf5-2a4b-4100-bf23-beb7f3c4c56f","order_by":4,"name":"Mohd Asif Shah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3OMQrCMBSA4SeFujxwFUrxBEJKIA4GzxIRMiuCOHZzc1YEz9DiBQIZugQ9QBfFC1RcBAeNowgNOjnkhyQk8PEC4PP9a4JzYo/XgkZqt8pNpHwjjZV7jNRfkG4anKujONDWRufXMfA4U8EprSNMhawtRMnaezmNViBppsLEQYDB8FZyMEgiBD3MFPSO9aR5rYTY845Bekd4WNK8OKYgsR9TjBhkdoqyBB0f0zizZEQTE876SEZ0rXFST4rF7nITg2Rrgl2J80G8LBZ5LYHg7UY+Xnw+n8/3S08ubkrTnLI70AAAAABJRU5ErkJggg==","orcid":"","institution":"Kardan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Mohd","middleName":"Asif","lastName":"Shah","suffix":""}],"badges":[],"createdAt":"2024-03-14 10:21:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4099720/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4099720/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69606664,"identity":"e0e6b3d8-9ec8-4b73-ac6f-1175dbb61866","added_by":"auto","created_at":"2024-11-22 07:24:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":919650,"visible":true,"origin":"","legend":"","description":"","filename":"CropYieldpredictionrajnism11.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4099720/v1_covered_4d5bf5f3-d7e9-4083-b576-03471e397155.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AgroAdvisor: Crop Yield Prediction, Crop and Fertilizer Recommendation System using Random Forest with Gradient Boosting and DeepFM for Precise Agriculture","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":"Crop Yield Prediction, Crop Recommendation System, Fertilizer Recommendation System, Integrated System, Deep Learning Algorithms, Machine Learning Algorithms, DeepFM, Random Forest","lastPublishedDoi":"10.21203/rs.3.rs-4099720/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4099720/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCrop yield prediction plays a very important role in productivity growth. 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