A Convolutional Neural Network Model and Algorithm Driven Prototype for Sustainable Tilling and Fertilizer Optimization

A Convolutional Neural Network Model and Algorithm Driven Prototype for Sustainable Tilling and Fertilizer Optimization | 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 A Convolutional Neural Network Model and Algorithm Driven Prototype for Sustainable Tilling and Fertilizer Optimization Sajeev Magesh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4698439/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Tilling, a common agricultural practice, is being done excessively on farms leading to about 2.35 billion tons of soil erosion from US croplands annually. This causes soil erosion, soil infertility, carbon release, nutrient runoff, and fertilizer over-usage. This paper evaluates whether optimizing tillage intensity, timing, and fertilizer quantity will address these problems. A convolutional neural network based machine learning model utilizes a camera-captured field image to determine existing tilling intensity on a 7-point scale. This machine learning output, along with soil sensor and external forecast data, flows into a 10-parameter algorithm that determines optimal tilling and fertilizer levels. A fully functional tractor prototype demonstrates the above. A 30-year simulation comparing conventionally-tilled and algorithm-tilled farms showed a reduction in carbon emission by 57%, fertilizer usage by 43%, and runoff by 86% demonstrating the transformative potential of this algorithm. Additionally, a stationary prototype was deployed in 155 farms across 5 countries. Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Ecology/Agri ecology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Nov, 2024 Reviews received at journal 15 Oct, 2024 Reviews received at journal 21 Sep, 2024 Reviewers agreed at journal 12 Sep, 2024 Reviewers agreed at journal 04 Sep, 2024 Reviewers invited by journal 26 Aug, 2024 Editor assigned by journal 21 Aug, 2024 Submission checks completed at journal 19 Aug, 2024 First submitted to journal 06 Jul, 2024 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-4698439","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":354151597,"identity":"e79e3982-44c4-4d1d-bee7-3f6351d491d1","order_by":0,"name":"Sajeev Magesh","email":"data:image/png;base64,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","orcid":"","institution":"Dublin High School","correspondingAuthor":true,"prefix":"","firstName":"Sajeev","middleName":"","lastName":"Magesh","suffix":""}],"badges":[],"createdAt":"2024-07-07 01:46:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4698439/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4698439/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64601742,"identity":"946823b1-1c35-4afb-8547-6d7916b2f337","added_by":"auto","created_at":"2024-09-16 12:13:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1536516,"visible":true,"origin":"","legend":"","description":"","filename":"npjSustainableAgricultureTillingResearchManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4698439/v1_covered_494ac463-7dd7-4daa-ac07-c17e99d58805.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Convolutional Neural Network Model and Algorithm Driven Prototype for Sustainable Tilling and Fertilizer Optimization","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":"npj-sustainable-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Sustainable Agriculture](https://www.nature.com/npjsustainagric/)","snPcode":"44264","submissionUrl":"https://submission.springernature.com/new-submission/44264/3","title":"npj Sustainable Agriculture","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4698439/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4698439/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTilling, a common agricultural practice, is being done excessively on farms leading to about 2.35\u0026nbsp;billion tons of soil erosion from US croplands annually. 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