Rapid and Non-destructive Evaluation of Soybean Seed Viability Using Transmission Hyperspectral Imaging and Ensemble Learning

Rapid and Non-destructive Evaluation of Soybean Seed Viability Using Transmission Hyperspectral Imaging and Ensemble Learning | 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 Rapid and Non-destructive Evaluation of Soybean Seed Viability Using Transmission Hyperspectral Imaging and Ensemble Learning Fangfang Liu, Pengshuai Niu, Wei Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9119890/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Traditional soybean seed viability assessment methods are destructive, time-intensive, and incapable of rapid, non-destructive single-seed grading. To overcome these limitations, this study proposes a novel approach integrating Transmission Hyperspectral Imaging (THSI) with ensemble learning for rapid, non-destructive evaluation. Naturally aged soybean seeds were analyzed using full-spectrum (400–2500 nm) transmittance data to capture deep physiological information. Multiple datasets were constructed by comparing preprocessing techniques—including Smoothing, First Derivative (FD), Hilbert Transform (HT), Savitzky–Golay, Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV)—with dimensionality reduction algorithms such as Principal Component Analysis (PCA), Successive Projections Algorithm (SPA), and Competitive Adaptive Reweighted Sampling (CARS). The proposed Stacking ensemble model integrates predictions from Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), k-Nearest Neighbors (k-NN), and Logistic Regression (LR), achieving 98.33% accuracy and an F1-score of 98.12% on the CARS-HT dataset—significantly outperforming individual classifiers in both accuracy and robustness. Furthermore, the model identified 17 critical wavelengths that reveal physiological mechanisms ranging from chlorophyll degradation and antioxidant balance in the visible spectrum to water migration and lipid peroxidation in the infrared region. The method's high precision and reliability were validated, providing robust technical support for intelligent and precise soybean seed quality management. Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Biological sciences/Plant sciences soybean vigor hyperspectral imaging stacking ensemble learning transmittance nondestructive detection Full Text Additional Declarations No competing interests reported. Supplementary Files partialdatasets.csv Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviews received at journal 14 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviews received at journal 08 May, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers invited by journal 27 Mar, 2026 Editor invited by journal 24 Mar, 2026 Editor assigned by journal 14 Mar, 2026 Submission checks completed at journal 14 Mar, 2026 First submitted to journal 14 Mar, 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. 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-9119890","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":613281649,"identity":"d93076bf-06c0-4b02-bd48-fe36d5c546e8","order_by":0,"name":"Fangfang Liu","email":"","orcid":"","institution":"Nanjing University of Industry Technology","correspondingAuthor":false,"prefix":"","firstName":"Fangfang","middleName":"","lastName":"Liu","suffix":""},{"id":613281650,"identity":"66c0c4a0-44c2-42da-965d-85323f9102cf","order_by":1,"name":"Pengshuai Niu","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Pengshuai","middleName":"","lastName":"Niu","suffix":""},{"id":613281651,"identity":"6e56bb21-d594-4b1a-9efd-9bc1f50060bd","order_by":2,"name":"Wei Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYFCCA1CCvbHx4QeStPDwHG42liDJKh6J9DYBHmLUmjMefiZdUXFHzl7yYRuDBIOdnG4DAS2WDcfMJM+ceWbMI53Y9qCAIdnY7AABLQYHDphJNrYdTuyRTmw3kGA4kLiNsJbj3yBaJA+2SfAQp+UM1BYJRuK1FFs2gPxyJhEYyAbE+OXG8Y03G4Ahxt5+/OHDDxV2cgS1MEigqDAgpBwE+BuIUTUKRsEoGAUjGgAAxMdIL15+/0sAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2026-03-14 05:54:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9119890/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9119890/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105729162,"identity":"d40b47b0-a4b6-432f-9220-ed0cd96e1bea","added_by":"auto","created_at":"2026-03-30 11:13:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1123765,"visible":true,"origin":"","legend":"","description":"","filename":"RapidandNondestructiveEvaluationofSoybeanSeedViability.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9119890/v1_covered_612fdbf2-c570-4619-8fd4-8b677df89a54.pdf"},{"id":105725015,"identity":"92e025c5-f238-4250-b7e4-0d825e19e024","added_by":"auto","created_at":"2026-03-30 10:17:35","extension":"csv","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":91417,"visible":true,"origin":"","legend":"","description":"","filename":"partialdatasets.csv","url":"https://assets-eu.researchsquare.com/files/rs-9119890/v1/075f82ef6ad5a83f59723313.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rapid and Non-destructive Evaluation of Soybean Seed Viability Using Transmission Hyperspectral Imaging and Ensemble Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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":"soybean vigor, hyperspectral imaging, stacking ensemble learning, transmittance, nondestructive detection","lastPublishedDoi":"10.21203/rs.3.rs-9119890/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9119890/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTraditional soybean seed viability assessment methods are destructive, time-intensive, and incapable of rapid, non-destructive single-seed grading. To overcome these limitations, this study proposes a novel approach integrating Transmission Hyperspectral Imaging (THSI) with ensemble learning for rapid, non-destructive evaluation. Naturally aged soybean seeds were analyzed using full-spectrum (400\u0026ndash;2500 nm) transmittance data to capture deep physiological information. Multiple datasets were constructed by comparing preprocessing techniques\u0026mdash;including Smoothing, First Derivative (FD), Hilbert Transform (HT), Savitzky\u0026ndash;Golay, Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV)\u0026mdash;with dimensionality reduction algorithms such as Principal Component Analysis (PCA), Successive Projections Algorithm (SPA), and Competitive Adaptive Reweighted Sampling (CARS). The proposed Stacking ensemble model integrates predictions from Random Forest (RF), Support Vector Machine (SVM), Na\u0026iuml;ve Bayes (NB), k-Nearest Neighbors (k-NN), and Logistic Regression (LR), achieving 98.33% accuracy and an F1-score of 98.12% on the CARS-HT dataset\u0026mdash;significantly outperforming individual classifiers in both accuracy and robustness. Furthermore, the model identified 17 critical wavelengths that reveal physiological mechanisms ranging from chlorophyll degradation and antioxidant balance in the visible spectrum to water migration and lipid peroxidation in the infrared region. The method's high precision and reliability were validated, providing robust technical support for intelligent and precise soybean seed quality management.\u003c/p\u003e","manuscriptTitle":"Rapid and Non-destructive Evaluation of Soybean Seed Viability Using Transmission Hyperspectral Imaging and Ensemble Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-30 10:17:25","doi":"10.21203/rs.3.rs-9119890/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-18T18:30:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T01:38:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247032378882120099772928114837519608164","date":"2026-05-11T02:05:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T17:14:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260539094347894566376918330454582266055","date":"2026-04-10T04:06:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-27T12:01:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-24T06:38:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-14T12:26:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-14T12:25:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-14T05:43:45+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":"6770954f-0496-444e-9067-d0ae913611a9","owner":[],"postedDate":"March 30th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-18T18:30:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T01:38:12+00:00","index":89,"fulltext":""},{"type":"reviewerAgreed","content":"247032378882120099772928114837519608164","date":"2026-05-11T02:05:06+00:00","index":87,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T17:14:46+00:00","index":82,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":65259418,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":65259419,"name":"Physical sciences/Mathematics and computing"},{"id":65259420,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-05-18T18:38:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-30 10:17:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9119890","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9119890","identity":"rs-9119890","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}