Research on a Multi-Task Monitoring Method for Shrimp Vitality Assessment

Research on a Multi-Task Monitoring Method for Shrimp Vitality Assessment | 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 Research on a Multi-Task Monitoring Method for Shrimp Vitality Assessment Yufeng Xie, Lei Wang, Xin Wu, Shuangle Wu, Weiming Cai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7953626/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 In intensive high-density recirculating aquaculture systems, real-time detection of shrimp vitality state and death time estimation are critical technical challenges for ensuring production efficiency and product quality. The decay of dead shrimp rapidly pollutes water, making timely death time determination essential to pinpoint pollution sources, reduce the time dead shrimp remain in the water, and mitigate the risk of mass mortality. This study presents a deep learning-based method for determining shrimp death state and predicting time of death. By developing an improved YOLOv11 object detection algorithm, we construct a multi-task detection network. Beyond enabling accurate classification into three vitality states (Living, Half-Dead, and Dead), the network incorporates key point detection to capture spatial variations of the shrimp head, body, and tail. Combined with the Cosine Theorem, postural angles characterizing body curl are calculated. A polynomial regression model is established to quantify the relationship between postural angle and death time. Experimental results on a dataset of 2800 image samples show that the model achieved a detection accuracy (F1-score) of 96%. The time of death prediction had a mean absolute error of 6.3 minutes. This method not only overcomes a limitation of traditional visual inspection, which can only determine life status, but also enables retrospective death time estimation through posture analysis. It offers a novel technical approach for intelligent aquaculture monitoring and quality assessment, significantly contributing to reducing production risks and enhancing the commercial value of shrimp products. Shrimp mortality detection Time of death estimation Keypoint detection YOLOv11 Aquaculture monitoring Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Nov, 2025 Reviews received at journal 04 Nov, 2025 Reviews received at journal 04 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 31 Oct, 2025 Reviewers invited by journal 29 Oct, 2025 Editor assigned by journal 29 Oct, 2025 Submission checks completed at journal 27 Oct, 2025 First submitted to journal 25 Oct, 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-7953626","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540706037,"identity":"ac65b3e9-caa2-40ca-bc9c-de1bd6a10853","order_by":0,"name":"Yufeng Xie","email":"","orcid":"","institution":"Zhejiang Sci-Tech University","correspondingAuthor":false,"prefix":"","firstName":"Yufeng","middleName":"","lastName":"Xie","suffix":""},{"id":540706038,"identity":"24c51bf2-3cb0-4e52-b9d5-2506e03626de","order_by":1,"name":"Lei Wang","email":"","orcid":"","institution":"NingboTech 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Assessment","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":"","identity":"aquaculture-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"10499","submissionUrl":"https://submission.nature.com/new-submission/10499/3","title":"Aquaculture International","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Shrimp mortality detection, Time of death estimation, Keypoint detection, YOLOv11,Aquaculture monitoring","lastPublishedDoi":"10.21203/rs.3.rs-7953626/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7953626/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In intensive high-density recirculating aquaculture systems, real-time detection of shrimp vitality state and death time estimation are critical technical challenges for ensuring production efficiency and product quality. The decay of dead shrimp rapidly pollutes water, making timely death time determination essential to pinpoint pollution sources, reduce the time dead shrimp remain in the water, and mitigate the risk of mass mortality. This study presents a deep learning-based method for determining shrimp death state and predicting time of death. By developing an improved YOLOv11 object detection algorithm, we construct a multi-task detection network. Beyond enabling accurate classification into three vitality states (Living, Half-Dead, and Dead), the network incorporates key point detection to capture spatial variations of the shrimp head, body, and tail. Combined with the Cosine Theorem, postural angles characterizing body curl are calculated. A polynomial regression model is established to quantify the relationship between postural angle and death time. Experimental results on a dataset of 2800 image samples show that the model achieved a detection accuracy (F1-score) of 96%. The time of death prediction had a mean absolute error of 6.3 minutes. This method not only overcomes a limitation of traditional visual inspection, which can only determine life status, but also enables retrospective death time estimation through posture analysis. It offers a novel technical approach for intelligent aquaculture monitoring and quality assessment, significantly contributing to reducing production risks and enhancing the commercial value of shrimp products.","manuscriptTitle":"Research on a Multi-Task Monitoring Method for Shrimp Vitality Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-07 08:53:41","doi":"10.21203/rs.3.rs-7953626/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-06T02:55:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-05T03:44:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-05T01:45:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43552091117853202877363097032806258521","date":"2025-11-03T06:52:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142529155143221153903378888564317786630","date":"2025-11-01T00:57:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-29T08:17:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-29T07:55:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-28T02:48:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Aquaculture International","date":"2025-10-25T12:29:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"","identity":"aquaculture-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"10499","submissionUrl":"https://submission.nature.com/new-submission/10499/3","title":"Aquaculture International","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b322371f-001f-4762-ab1a-bf20fa8c87ec","owner":[],"postedDate":"November 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-10T15:39:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-07 08:53:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7953626","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7953626","identity":"rs-7953626","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}