Estimating biomass density in land-based cultivation of  Ulva spp. using a low cost RGB imaging system

Estimating biomass density in land-based cultivation of Ulva spp. using a low cost RGB imaging system | 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 Estimating biomass density in land-based cultivation of Ulva spp. using a low cost RGB imaging system Joost van Dalen, Joseph Peller, Reindert Wieger Nauta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9279578/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 Seaweed cultivation faces scalability challenges due to labor-intensive biomass monitoring. Here, we demonstrate a low cost RGB imaging system for Ulva spp biomass estimation (0.5-5.0 g L −1 ) in land-based raceways. Using a gener- alized segmentation model, we extracted area and color features as predictors for linear and log-linear regressions. Surface area proved the strongest predictor (R 2 = 0.99, p = <.001, RMSE = 0.18 g L −1 ), with log-linear models outper- forming linear regressions (R 2 = 0.88, p = <.001, RMSE = 0.50 g L −1 ) in per revolution analysis. Aggregating frame-level data into three-minute revolutions reduced segmentation errors and accounted for random seaweed distribution, enabling accurate biomass density predictions. This accessible, scalable approach offers a practical solution to reduce labor costs and optimize yields in precision aquaculture. Biomass estimation Ulva spp macroalgae machine vision Segment Anything Model (SAM) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 05 May, 2026 Reviews received at journal 24 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 14 Apr, 2026 Editor assigned by journal 14 Apr, 2026 Submission checks completed at journal 13 Apr, 2026 First submitted to journal 31 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. 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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-9279578","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624424750,"identity":"ae328898-f7f5-4bf2-9826-16e6ebd69f20","order_by":0,"name":"Joost van Dalen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACxgY4M4HxAAODDYT9gJCWAxAtIDoNqp2QVUhaDhPWwtzefPjzB4bD8ubsyQ8OfNxxPnHD7QNsD/BpYew5liZxgOGw4c6eZwYHZ565nbjhXAK7AV4tM3LMgA5KY9xwI8HgMG/b7dwNZxjYJAhoMf4A1GK/4Ub6h8N/284RpcUA6DCbxA03cgwOM7YdIEILyC9nDGySN5x5U3Cwty25fuYZxja8WgyBIfahokLCdsPx9I0PfrbZGfOdYT4m8QGflgYQaYBqcwMeDQwM8nhlR8EoGAWjYBSAAACWA1k0tzTjJgAAAABJRU5ErkJggg==","orcid":"","institution":"Wageningen University \u0026 Research","correspondingAuthor":true,"prefix":"","firstName":"Joost","middleName":"van","lastName":"Dalen","suffix":""},{"id":624424751,"identity":"88589642-92a1-4e1e-b3d3-ee7f538f28ae","order_by":1,"name":"Joseph Peller","email":"","orcid":"","institution":"Wageningen University \u0026 Research","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Peller","suffix":""},{"id":624424752,"identity":"bbf32566-95f6-46c1-b59d-303973cd005e","order_by":2,"name":"Reindert Wieger Nauta","email":"","orcid":"","institution":"Wageningen University \u0026 Research","correspondingAuthor":false,"prefix":"","firstName":"Reindert","middleName":"Wieger","lastName":"Nauta","suffix":""}],"badges":[],"createdAt":"2026-03-31 12:10:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9279578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9279578/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107490069,"identity":"351e6c40-3407-4719-b515-0d06bdef454b","added_by":"auto","created_at":"2026-04-22 02:49:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6395227,"visible":true,"origin":"","legend":"","description":"","filename":"SeaweedSightSpringer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9279578/v1_covered_dd4544ee-2753-418a-886b-26e9d2fba2e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimating biomass density in land-based cultivation of Ulva spp. using a low cost RGB imaging system","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":"","identity":"journal-of-applied-phycology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"10811","submissionUrl":"https://submission.nature.com/new-submission/10811/3","title":"Journal of Applied Phycology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Biomass estimation, Ulva spp, macroalgae, machine vision, Segment Anything Model (SAM)","lastPublishedDoi":"10.21203/rs.3.rs-9279578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9279578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSeaweed cultivation faces scalability challenges due to labor-intensive biomass monitoring. Here, we demonstrate a low cost RGB imaging system for Ulva spp biomass estimation (0.5-5.0 g L\u003csup\u003e−1\u003c/sup\u003e \u0026nbsp;) in land-based raceways. Using a gener- alized segmentation model, we extracted area and color features as predictors for linear and log-linear regressions. Surface area proved the strongest predictor (R\u003csup\u003e2\u003c/sup\u003e = 0.99, p = \u0026lt;.001, RMSE = 0.18 g L\u003csup\u003e−1\u003c/sup\u003e \u0026nbsp;), with log-linear models outper- forming linear regressions (R\u003csup\u003e2\u003c/sup\u003e = 0.88, p = \u0026lt;.001, RMSE = 0.50 g L\u003csup\u003e−1 \u003c/sup\u003e\u0026nbsp;) in per revolution analysis. Aggregating frame-level data into three-minute revolutions reduced segmentation errors and accounted for random seaweed distribution, enabling accurate biomass density predictions. 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