Enhancing Soil Fertility Prediction Through Advanced Modelling: Agrimind Intelligent Fertility Predictor

Enhancing Soil Fertility Prediction Through Advanced Modelling: Agrimind Intelligent Fertility Predictor | 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 Enhancing Soil Fertility Prediction Through Advanced Modelling: Agrimind Intelligent Fertility Predictor Raghavendra Rao RV, Srinivasulu Reddy U This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8539666/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Soil fertility prediction (SFP) is a critical process that estimates nutrient availability in soil, directly impacting agricultural productivity and crop health. Achieving accurate SFP is essential for optimising agricultural productivity. However, traditional models face significant challenges due to the complex nature of soil composition, dynamic environmental processes and limited availability of high quality data. To address these issues, this study proposes the AgriMind Intelligent Fertility Predictor (AMIFP), an advanced predictive model that combines innovative preprocessing and deep learning techniques to enhance SFPs. This methodology introduces a novel preprocessing approach, SMOTEImputeScaler (SIC), which addresses missing data, normalises features and mitigates class imbalance by generating synthetic samples near class boundaries. Additionally, introduces the model Attentive LSTM Aware Dense Network (ALADN), an advanced modelling framework that ensures robust feature extraction and effective classification, ultimately enhancing the accuracy and reliability of SFPs. To optimise hyperparameters effectively, this work introduces Genetic probabilistic Search Hyperparameter Optimization (GPSHO), which improves model performance by exploring complex hyperparameter spaces while reducing computational overhead. Experimental results demonstrate that the AMIFP model significantly outperforms existing approaches, achieving 98.65% accuracy, 98.73% precision, 98.65% recall, 98.70% F1, and mean square error (MSE) of 0.007, indicating that the proposed AMIFP model offers a robust and reliable solution for SFP, aiding agricultural decision-making processes. Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Deep Learning (DL) AgriMind Intelligent Fertility prediction Attentive LSTM Aware Dense Network Hyper Parameter Optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 Feb, 2026 Reviews received at journal 17 Jan, 2026 Reviews received at journal 16 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers invited by journal 15 Jan, 2026 Editor invited by journal 15 Jan, 2026 Editor assigned by journal 08 Jan, 2026 Submission checks completed at journal 08 Jan, 2026 First submitted to journal 07 Jan, 2026 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. 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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-8539666","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":576203356,"identity":"02a7e22b-04c7-4582-9cf5-c97042baa583","order_by":0,"name":"Raghavendra Rao RV","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYPACmwQ4k41ILWmkazmcQEgFApjPPsC64UfN+Tzd/sMPHzDU2DHwSTfg1yJzLoHtZs+x28VmN9KMDRiOJTOwyRzAr0WCh4HtNhAlbrvBwybBwHaAgU2CgCMhWv6dS9x2/gz7D4Z/xGphbDuQuO1ADhsDkEGMFsa2m719yWC/SCT2JfMQoYX52I0f3+zyzM4ffvjhwzc7OfkZBLQwMDA2INhAxTyE1I+CUTAKRsEoIAIAACdEPIFq81owAAAAAElFTkSuQmCC","orcid":"","institution":"B.M.S. 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Fertility Predictor\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Deep Learning (DL), AgriMind Intelligent Fertility prediction, Attentive LSTM Aware Dense Network, Hyper Parameter Optimization","lastPublishedDoi":"10.21203/rs.3.rs-8539666/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8539666/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil fertility prediction (SFP) is a critical process that estimates nutrient availability in soil, directly impacting agricultural productivity and crop health. Achieving accurate SFP is essential for optimising agricultural productivity. However, traditional models face significant challenges due to the complex nature of soil composition, dynamic environmental processes and limited availability of high quality data. To address these issues, this study proposes the AgriMind Intelligent Fertility Predictor (AMIFP), an advanced predictive model that combines innovative preprocessing and deep learning techniques to enhance SFPs. This methodology introduces a novel preprocessing approach, SMOTEImputeScaler (SIC), which addresses missing data, normalises features and mitigates class imbalance by generating synthetic samples near class boundaries. Additionally, introduces the model Attentive LSTM Aware Dense Network (ALADN), an advanced modelling framework that ensures robust feature extraction and effective classification, ultimately enhancing the accuracy and reliability of SFPs. To optimise hyperparameters effectively, this work introduces Genetic probabilistic Search Hyperparameter Optimization (GPSHO), which improves model performance by exploring complex hyperparameter spaces while reducing computational overhead. Experimental results demonstrate that the AMIFP model significantly outperforms existing approaches, achieving 98.65% accuracy, 98.73% precision, 98.65% recall, 98.70% F1, and mean square error (MSE) of 0.007, indicating that the proposed AMIFP model offers a robust and reliable solution for SFP, aiding agricultural decision-making processes.\u003c/p\u003e","manuscriptTitle":"Enhancing Soil Fertility Prediction Through Advanced Modelling: Agrimind Intelligent Fertility Predictor","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 11:43:36","doi":"10.21203/rs.3.rs-8539666/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-05T06:11:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-17T20:15:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-16T22:17:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73654851669661628589676612442712744662","date":"2026-01-16T22:15:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57900852077951932997061159139317704037","date":"2026-01-16T09:33:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-16T03:50:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-16T03:24:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-09T03:02:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-09T03:00:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-07T09:39:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ffa2594b-d773-42b9-8b88-7a7678209ad7","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":61431257,"name":"Earth and environmental sciences/Environmental sciences"},{"id":61431258,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-05-04T15:59:13+00:00","versionOfRecord":{"articleIdentity":"rs-8539666","link":"https://doi.org/10.1038/s41598-026-50366-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-28 15:57:18","publishedOnDateReadable":"April 28th, 2026"},"versionCreatedAt":"2026-01-20 11:43:36","video":"","vorDoi":"10.1038/s41598-026-50366-9","vorDoiUrl":"https://doi.org/10.1038/s41598-026-50366-9","workflowStages":[]},"version":"v1","identity":"rs-8539666","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8539666","identity":"rs-8539666","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}