OneRosette to Predict Them All: Single Plant Prompting on a Visual Foundation Model to Segment Symptomatic Arabidopsis Thaliana Time Series

OneRosette to Predict Them All: Single Plant Prompting on a Visual Foundation Model to Segment Symptomatic Arabidopsis Thaliana Time Series | 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 Method Article OneRosette to Predict Them All: Single Plant Prompting on a Visual Foundation Model to Segment Symptomatic Arabidopsis Thaliana Time Series Felicià Maviane Macia, Sabine Wiedemann-Merdinoglu, David Rousseau, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6505262/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Oct, 2025 Read the published version in Plant Methods → Version 1 posted 13 You are reading this latest preprint version Abstract Background Arabidopsis thaliana is the leading model plant used to study plant-pathogen interactions. High-throughput phenotyping allows for the simultaneous study of many plants with high-frequency image acquisition. Nevertheless, the segmentation of symptomatic plants on natural soil remains challenging, requiring the annotation of hundreds of images and the subsequent training of specialized models for each pathosystem considered. This paper presents a novel approach to segmenting A. thaliana plants' time series using a single annotated image. Results Images of A. thaliana plants infected with Pseudomonas syringae pathovar tomato strain DC3000 were annotated with precise segmentation masks. We compared various mask segmentation methods; our one-shot learning approach obtained a Dice score of 0.977 on our test dataset. Variables extracted from the segmented images allowed statistical discrimination between infected and control plants. We used our one-shot learning approach without further fine-tuning on a new pathosystem; A. thaliana infected with Ralstonia pseudosolanacearum , strain GMI1000. We obtained a Dice score of 0.966 in the second test dataset. We also obtained a Pearson correlation coefficient of -0.928 between the annotated quantitative disease index and the variable generated with our method. Conclusion This work provides a pipeline to segment symptomatic A. thaliana plants by leveraging a visual foundation model. The method has been used successfully on two different pathogens, is fast to train, and does not need a large dedicated graphical processing unit. Our method has characterized plant-pathogen interactions of two pathosystems without fine-tuning for the second pathosystem. Its ease of use and low computing requirements should make adapting our approach to other high-throughput phenotyping platforms easy. Deep learning Visual foundation model Arabidopsis thaliana Pseudomonas syringae pv. tomato DC3000 Ralstonia pseudosolanacearum Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Oct, 2025 Read the published version in Plant Methods → Version 1 posted Editorial decision: Revision requested 11 Jun, 2025 Reviews received at journal 29 May, 2025 Reviews received at journal 29 May, 2025 Reviews received at journal 24 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers agreed at journal 06 May, 2025 Reviews received at journal 05 May, 2025 Reviewers agreed at journal 04 May, 2025 Reviewers agreed at journal 02 May, 2025 Reviewers invited by journal 02 May, 2025 Editor assigned by journal 24 Apr, 2025 Submission checks completed at journal 24 Apr, 2025 First submitted to journal 22 Apr, 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. 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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-6505262","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":452796797,"identity":"e883c78d-3e56-45e9-a0e3-b4fff6d2a17d","order_by":0,"name":"Felicià Maviane Macia","email":"","orcid":"","institution":"Laboratoire des Interactions Plantes Micro-organismes Environnement (LIPME)","correspondingAuthor":false,"prefix":"","firstName":"Felicià","middleName":"Maviane","lastName":"Macia","suffix":""},{"id":452796798,"identity":"d9da7709-6e2b-4707-816a-b0fc459c87d4","order_by":1,"name":"Sabine Wiedemann-Merdinoglu","email":"","orcid":"","institution":"University of Strasbourg","correspondingAuthor":false,"prefix":"","firstName":"Sabine","middleName":"","lastName":"Wiedemann-Merdinoglu","suffix":""},{"id":452796799,"identity":"b692ce6b-5ec6-4a98-91d3-d998e28bd0e8","order_by":2,"name":"David Rousseau","email":"data:image/png;base64,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","orcid":"","institution":"Research Institute of Horticulture and Seeds","correspondingAuthor":true,"prefix":"","firstName":"David","middleName":"","lastName":"Rousseau","suffix":""},{"id":452796800,"identity":"37b0fe51-c8a1-4121-9880-d024b3e70ed1","order_by":3,"name":"Nemo Peeters","email":"","orcid":"","institution":"Laboratoire des Interactions Plantes Micro-organismes Environnement (LIPME)","correspondingAuthor":false,"prefix":"","firstName":"Nemo","middleName":"","lastName":"Peeters","suffix":""}],"badges":[],"createdAt":"2025-04-22 14:23:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6505262/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6505262/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13007-025-01432-2","type":"published","date":"2025-10-09T15:57:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93419980,"identity":"5064db82-ad43-4c29-a405-e04397a355b1","added_by":"auto","created_at":"2025-10-13 16:09:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4431147,"visible":true,"origin":"","legend":"","description":"","filename":"2024OneRosette.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6505262/v1_covered_1288cc41-9907-42e7-bf32-86a33efb7cfa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"OneRosette to Predict Them All: Single Plant Prompting on a Visual Foundation Model to Segment Symptomatic Arabidopsis Thaliana Time Series","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":"plant-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plme","sideBox":"Learn more about [Plant Methods](http://plantmethods.biomedcentral.com/)","snPcode":"13007","submissionUrl":"https://submission.nature.com/new-submission/13007/3","title":"Plant Methods","twitterHandle":"@PlantMethods","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Deep learning, Visual foundation model, Arabidopsis thaliana, Pseudomonas syringae pv. tomato DC3000, Ralstonia pseudosolanacearum","lastPublishedDoi":"10.21203/rs.3.rs-6505262/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6505262/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e \u003cem\u003eArabidopsis thaliana\u003c/em\u003e is the leading model plant used to study plant-pathogen interactions. High-throughput phenotyping allows for the simultaneous study of many plants with high-frequency image acquisition. Nevertheless, the segmentation of symptomatic plants on natural soil remains challenging, requiring the annotation of hundreds of images and the subsequent training of specialized models for each pathosystem considered. This paper presents a novel approach to segmenting \u003cem\u003eA. thaliana\u003c/em\u003e plants' time series using a single annotated image.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Images of \u003cem\u003eA. thaliana\u003c/em\u003e plants infected with \u003cem\u003ePseudomonas syringae\u003c/em\u003e pathovar \u003cem\u003etomato\u003c/em\u003e strain DC3000 were annotated with precise segmentation masks. We compared various mask segmentation methods; our one-shot learning approach obtained a Dice score of 0.977 on our test dataset. Variables extracted from the segmented images allowed statistical discrimination between infected and control plants. We used our one-shot learning approach without further fine-tuning on a new pathosystem; \u003cem\u003eA. thaliana\u003c/em\u003e infected with \u003cem\u003eRalstonia pseudosolanacearum\u003c/em\u003e, strain GMI1000. We obtained a Dice score of 0.966 in the second test dataset. We also obtained a Pearson correlation coefficient of -0.928 between the annotated quantitative disease index and the variable generated with our method.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e This work provides a pipeline to segment symptomatic \u003cem\u003eA. thaliana\u003c/em\u003e plants by leveraging a visual foundation model. The method has been used successfully on two different pathogens, is fast to train, and does not need a large dedicated graphical processing unit. Our method has characterized plant-pathogen interactions of two pathosystems without fine-tuning for the second pathosystem. Its ease of use and low computing requirements should make adapting our approach to other high-throughput phenotyping platforms easy.\u003c/p\u003e","manuscriptTitle":"OneRosette to Predict Them All: Single Plant Prompting on a Visual Foundation Model to Segment Symptomatic Arabidopsis Thaliana Time Series","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 05:35:25","doi":"10.21203/rs.3.rs-6505262/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-12T02:46:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-29T17:10:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-29T16:49:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-24T12:58:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41266015861256301506055031078602492221","date":"2025-05-14T13:21:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54333704903988032130602001484125286773","date":"2025-05-06T15:49:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-05T10:00:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55304641166153364947347897710375476969","date":"2025-05-04T11:29:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148985442477593220237645210709651741851","date":"2025-05-02T11:25:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-02T11:17:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-24T23:28:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-24T07:10:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Methods","date":"2025-04-22T14:21:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plme","sideBox":"Learn more about [Plant Methods](http://plantmethods.biomedcentral.com/)","snPcode":"13007","submissionUrl":"https://submission.nature.com/new-submission/13007/3","title":"Plant Methods","twitterHandle":"@PlantMethods","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d0c41f0-34ce-4f75-85ac-142d8c793a5d","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:05:37+00:00","versionOfRecord":{"articleIdentity":"rs-6505262","link":"https://doi.org/10.1186/s13007-025-01432-2","journal":{"identity":"plant-methods","isVorOnly":false,"title":"Plant Methods"},"publishedOn":"2025-10-09 15:57:22","publishedOnDateReadable":"October 9th, 2025"},"versionCreatedAt":"2025-05-07 05:35:25","video":"","vorDoi":"10.1186/s13007-025-01432-2","vorDoiUrl":"https://doi.org/10.1186/s13007-025-01432-2","workflowStages":[]},"version":"v1","identity":"rs-6505262","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6505262","identity":"rs-6505262","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}