Non-destructive Detection of Spotted Wing Drosophila Infestation in Blueberries Using Hyperspectral Imaging

Non-destructive Detection of Spotted Wing Drosophila Infestation in Blueberries Using Hyperspectral Imaging | 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 Non-destructive Detection of Spotted Wing Drosophila Infestation in Blueberries Using Hyperspectral Imaging Xinyang Mu, Yuzhen Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6055969/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 Spotted Wing Drosophila (SWD) infestation in blueberries presents a significant threat to blueberry industries due to significant yield loss and quality/safety issues during postharvest process where there is zero-tolerance for infested fruit. Current detection methods require destructive sampling and are time-consuming and labor-intensive and thus not suitable for high-volume inspection of individual products during postharvest handling. This study presents an innovative hyperspectral imaging-based approach to detecting SWD infestation in highbush blueberry fruit. Two benchtop hyperspectral imaging systems in reflectance mode, operating in the visible-near-infrared (Vis-NIR, 400-1000 nm) and short-wavelength infrared (SWIR, 900-1700 nm) ranges respectively, were in-house assembled for acquiring images of 945 (including 706 healthy and 235 infested) blueberry samples hand-picked from orchards. Hyperspectral imagery was processed to segment blueberries and extract mean spectra from individual samples. Infested blueberries showed lower spectral reflectance in the region of 750 – 1350 nm than normal samples. Baseline models were built using six different classifiers for sample classification, and the models based on partial least squares discriminant analysis (PLS-DA) yielded the best overall accuracy of 90.2% and 92.5% for the Vis-NIR and SWIR systems, respectively, with the corresponding recall rates of 74.2% and 80.6% for infested fruit. Three alternative model pipelines were proposed by implementing oversampling of the minority (infested) fruit class and waveband selection, through an exhaustive search for optimal methods, resulting in improved detection performance. Oversampling was generally more effective than waveband selection for enhancing model performance, and their combination (oversampling followed by waveband selection) yielded the best classification, with PLS-DA remaining the best classifier. The Vis-NIR and SWIR systems achieved the best overall accuracies of 93.7% and 97.2%, respectively, with the corresponding recall rates of 85.9% and 95.7% for infested fruit. This research showed that hyperspectral imaging, especially in the SWIR range, is useful for rapid, non-destructive detection of SWD infestation in blueberry fruit. Agricultural Engineering Blueberry Spotted Wing Drosophila Imaging Spectroscopy Machine Learning Postharvest Full Text Additional Declarations The authors declare no competing interests. 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-6055969","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":417463944,"identity":"74ca671b-29e2-442b-b740-d230f41d43cf","order_by":0,"name":"Xinyang Mu","email":"","orcid":"","institution":"Michigan State University","correspondingAuthor":false,"prefix":"","firstName":"Xinyang","middleName":"","lastName":"Mu","suffix":""},{"id":417463945,"identity":"c7d4e52a-5fc7-4e7e-a2ab-b52074c7e612","order_by":1,"name":"Yuzhen Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACPhDB2MDAwA/hMxPWwgbTItlAshaDA0Rrkcgxk/y5wybP+NrxZxIMFdaJDcRokZA8k1ZsdhvIYDiTTqQWw7bDidtu57BJMAIZxGlJbPufuHl2+jMJxn/EajnYdiBxg3SCmQRjAzFaeJ4VWza2JSfOuJ1jbJFwLN2YoBZ+9uSNN3+22SX2z05/eONDjbUsQS0MDBwGCHYCYeUgwP6AOHWjYBSMglEwcgEACOk6vVHIrpwAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-3636-1137","institution":"Michigan State University","correspondingAuthor":true,"prefix":"","firstName":"Yuzhen","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2025-02-18 12:01:11","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6055969/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6055969/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76722056,"identity":"4fd9a67c-c58a-4e1d-aa73-2a76b5e5c239","added_by":"auto","created_at":"2025-02-20 04:46:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4437489,"visible":true,"origin":"","legend":"","description":"","filename":"BlueberrySWDHyperspectral.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6055969/v1_covered_5b745b1e-d202-436c-b4a8-98671fc0bd7e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eNon-destructive Detection of Spotted Wing Drosophila Infestation in Blueberries Using Hyperspectral Imaging\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Blueberry, Spotted Wing Drosophila, Imaging Spectroscopy, Machine Learning, Postharvest","lastPublishedDoi":"10.21203/rs.3.rs-6055969/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6055969/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSpotted Wing Drosophila (SWD) infestation in blueberries presents a significant threat to blueberry industries due to significant yield loss and quality/safety issues during postharvest process where there is zero-tolerance for infested fruit. Current detection methods require destructive sampling and are time-consuming and labor-intensive and thus not suitable for high-volume inspection of individual products during postharvest handling. This study presents an innovative hyperspectral imaging-based approach to detecting SWD infestation in highbush blueberry fruit. Two benchtop hyperspectral imaging systems in reflectance mode, operating in the visible-near-infrared (Vis-NIR, 400-1000 nm) and short-wavelength infrared (SWIR, 900-1700 nm) ranges respectively, were in-house assembled for acquiring images of 945 (including 706 healthy and 235 infested) blueberry samples hand-picked from orchards. Hyperspectral imagery was processed to segment blueberries and extract mean spectra from individual samples. Infested blueberries showed lower spectral reflectance in the region of 750 – 1350 nm than normal samples. Baseline models were built using six different classifiers for sample classification, and the models based on partial least squares discriminant analysis (PLS-DA) yielded the best overall accuracy of 90.2% and 92.5% for the Vis-NIR and SWIR systems, respectively, with the corresponding recall rates of 74.2% and 80.6% for infested fruit. Three alternative model pipelines were proposed by implementing oversampling of the minority (infested) fruit class and waveband selection, through an exhaustive search for optimal methods, resulting in improved detection performance. Oversampling was generally more effective than waveband selection for enhancing model performance, and their combination (oversampling followed by waveband selection) yielded the best classification, with PLS-DA remaining the best classifier. The Vis-NIR and SWIR systems achieved the best overall accuracies of 93.7% and 97.2%, respectively, with the corresponding recall rates of 85.9% and 95.7% for infested fruit. This research showed that hyperspectral imaging, especially in the SWIR range, is useful for rapid, non-destructive detection of SWD infestation in blueberry fruit.\u003c/p\u003e","manuscriptTitle":"Non-destructive Detection of Spotted Wing Drosophila Infestation in Blueberries Using Hyperspectral Imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-20 04:37:54","doi":"10.21203/rs.3.rs-6055969/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"68f77d30-6c0e-4ace-b780-51b8c6d84c01","owner":[],"postedDate":"February 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44506942,"name":"Agricultural Engineering"}],"tags":[],"updatedAt":"2025-02-20T04:37:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-20 04:37:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6055969","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6055969","identity":"rs-6055969","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}