Integrating Geophysics and Machine Learning for Soil Compaction Estimation in Agriculture

Integrating Geophysics and Machine Learning for Soil Compaction Estimation in 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 Research Article Integrating Geophysics and Machine Learning for Soil Compaction Estimation in Agriculture Jasper Tembeck Mbah, Katarzyna Pentoś, Krzysztof Pieczarka, Tomasz Wojciechowski This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8028888/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Soil compaction is a significant factor affecting crop yield; however, traditional assessment methods are labor-intensive and costly. To address this issue, rapid and cost-effective estimation techniques are needed. Geophysical methods are commonly used to predict various soil parameters, yet assessing parameters related to soil compaction remains challenging. Thus, integrating geophysical methods with machine learning presents a promising alternative to pedophysical models. This study examined how machine learning techniques — such as neural networks, support vector machines, and extreme gradient boosting — can predict penetration resistance, a key indicator of soil compaction. The models were developed using geophysical data from Geonics EM38 and Veris 3100 scanners, along with soil texture-related coefficients. Penetration resistance was measured at various soil depths ranging from the surface to 0.5 m. The most accurate models were achieved with the support vector machine algorithm for penetration resistance analyzed in soil layers from 0 to 0.4 m, utilizing soil apparent electrical conductivity and magnetic susceptibility measured with the EM38 scanner at a depth of 0.5 m, electrical conductivity from the Veris 3100 scanner, and the soil texture coefficient as independent variables. For these models, the mean percentage error (MAPE) for the test data fell between 12% and 23%, while the correlation coefficient R ranged from 0.66 to 0.79. The resulting models can be practically applied to plan tillage at various depths, optimizing fuel consumption without compromising soil conditions necessary for plant root system development. electrical conductivity soil compaction neural network support vector machine extreme gradient boosting Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Nov, 2025 Reviews received at journal 20 Nov, 2025 Reviews received at journal 18 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviewers invited by journal 12 Nov, 2025 Editor assigned by journal 11 Nov, 2025 Submission checks completed at journal 11 Nov, 2025 First submitted to journal 04 Nov, 2025 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-8028888","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":548571202,"identity":"27d517ea-d8e1-4480-951f-91105313dd22","order_by":0,"name":"Jasper Tembeck 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To address this issue, rapid and cost-effective estimation techniques are needed. Geophysical methods are commonly used to predict various soil parameters, yet assessing parameters related to soil compaction remains challenging. Thus, integrating geophysical methods with machine learning presents a promising alternative to pedophysical models. This study examined how machine learning techniques \u0026mdash; such as neural networks, support vector machines, and extreme gradient boosting \u0026mdash; can predict penetration resistance, a key indicator of soil compaction. The models were developed using geophysical data from Geonics EM38 and Veris 3100 scanners, along with soil texture-related coefficients. Penetration resistance was measured at various soil depths ranging from the surface to 0.5 m. The most accurate models were achieved with the support vector machine algorithm for penetration resistance analyzed in soil layers from 0 to 0.4 m, utilizing soil apparent electrical conductivity and magnetic susceptibility measured with the EM38 scanner at a depth of 0.5 m, electrical conductivity from the Veris 3100 scanner, and the soil texture coefficient as independent variables. For these models, the mean percentage error (MAPE) for the test data fell between 12% and 23%, while the correlation coefficient R ranged from 0.66 to 0.79. The resulting models can be practically applied to plan tillage at various depths, optimizing fuel consumption without compromising soil conditions necessary for plant root system development.\u003c/p\u003e","manuscriptTitle":"Integrating Geophysics and Machine Learning for Soil Compaction Estimation in Agriculture","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-24 06:30:07","doi":"10.21203/rs.3.rs-8028888/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-21T07:58:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-20T14:14:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-18T06:25:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70089597516428265543302225582936391791","date":"2025-11-17T07:36:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153271501873118633890833660822546788510","date":"2025-11-17T00:14:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18076912875342169956459603569479360440","date":"2025-11-16T21:02:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212632921734279991254405770168809905138","date":"2025-11-16T20:04:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-12T22:30:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-12T04:23:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-12T00:52:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of the Saudi Society of Agricultural Sciences","date":"2025-11-04T12:36:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-the-saudi-society-of-agricultural-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of the Saudi Society of Agricultural Sciences](https://link.springer.com/journal/44447)","snPcode":"44447","submissionUrl":"https://submission.springernature.com/new-submission/44447/3","title":"Journal of the Saudi Society of Agricultural Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10d4a0ca-e656-474a-a814-2d770e346130","owner":[],"postedDate":"November 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-05T09:39:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-24 06:30:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8028888","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8028888","identity":"rs-8028888","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}