Prediction accuracy and heritability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location

Prediction accuracy and heritability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location | 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 Prediction accuracy and heritability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location Daniel T.L Smith, Qiaomin Chen, Andries B Potgieter, Scott C Chapman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3889721/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Aug, 2024 Read the published version in Plant Methods → Version 1 posted 7 You are reading this latest preprint version Abstract Background This study explores the use of Unmanned Aerial Vehicles (UAVs) for estimating wheat biomass, focusing on the impact of phenotyping and analytical protocols. It emphasizes the importance of variable selection, model specificity, and sampling location within the experimental plot in predicting biomass, aiming to refine UAV-based estimation techniques for enhanced selection accuracy and throughput in variety testing and breeding programs. Results The research uncovered that integrating geometric and spectral traits with a partial least squares regression (PLSR) based variable selection workflow notably enhanced biomass prediction accuracy. A key finding was that models, tailored to specific maturity stages (vegetative, flowering, and grain-fill) were more accurate than those modelling the entire growth season for estimation of biomass at corresponding stages. However, experiment specific models did not significantly increase accuracy. The comparison between a permanent and a precise region of interest (ROI) within the plot showed negligible differences in biomass prediction accuracy, indicating the robustness of the approach across different sampling locations within the plot. Significant differences in the broad-sense heritability (H2) of biomass predictions across different experiments highlighted the need for further investigation into the optimal timing of measurement for prediction. Conclusions The study highlights the promising potential of UAV technology in biomass prediction for wheat at a small plot scale. It suggests that the accuracy of biomass predictions can be significantly improved through optimizing analytical and modelling protocols (i.e., variable selection, algorithm selection, stage-specific model development). Future work should focus on exploring the applicability of these findings under a wider variety of conditions and from a more diverse set of genotypes. Wheat Biomass estimation UAV based monitoring High throughput Phenotyping Field-Based Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Aug, 2024 Read the published version in Plant Methods → Version 1 posted Editorial decision: Revision requested 01 Mar, 2024 Reviews received at journal 03 Feb, 2024 Reviewers agreed at journal 03 Feb, 2024 Reviewers invited by journal 03 Feb, 2024 Editor assigned by journal 24 Jan, 2024 Submission checks completed at journal 24 Jan, 2024 First submitted to journal 22 Jan, 2024 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. 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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-3889721","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":269102082,"identity":"f659c784-c7b8-4f9a-b6a3-d01c0617e784","order_by":0,"name":"Daniel T.L Smith","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYDCCAyBUAGQwM7AxfqgAixkQoQWkhpmZjVniDJFaoGqY2Rh424jQwnf87MEDHwwY7HXb+Y89kJx3WLaBvXmbBEPNYZxaJM/kJRycYcCQuO0wM7tB4bbDxg08x8okGI7h1mJwIMfgMI8BQ4LZYWY2CclthxMbJHLMJBjY8Gg5/8bg8B+gw8BaeOcAtci/AWr5h0fLDaAtQP8ybgNraQDZwmMmwdiGxy833hgc7DGQAPnFTFriWLpxG09asUViXzpOLXznc4w//KiwsTc7f/CZ5Icaa9l+9sMbb3z4Zo1TCxRIwFmM4KhJIKQBGTA2kKJ6FIyCUTAKRgQAAF8rUzGgqAkBAAAAAElFTkSuQmCC","orcid":"","institution":"School of Agriculture and food Sustainability, The University of Queensland","correspondingAuthor":true,"prefix":"","firstName":"Daniel","middleName":"T.L","lastName":"Smith","suffix":""},{"id":269102083,"identity":"df6fe30a-85fe-4e5b-861e-89ab3a2754dc","order_by":1,"name":"Qiaomin Chen","email":"","orcid":"","institution":"School of Agriculture and food Sustainability, The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Qiaomin","middleName":"","lastName":"Chen","suffix":""},{"id":269102084,"identity":"8b6de939-d9f7-4757-b0e2-bae90db17092","order_by":2,"name":"Andries B Potgieter","email":"","orcid":"","institution":"Center for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Andries","middleName":"B","lastName":"Potgieter","suffix":""},{"id":269102085,"identity":"ea158adc-1377-4912-a785-c751fca25f0c","order_by":3,"name":"Scott C Chapman","email":"","orcid":"","institution":"School of Agriculture and food Sustainability, The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Scott","middleName":"C","lastName":"Chapman","suffix":""}],"badges":[],"createdAt":"2024-01-23 03:29:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3889721/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3889721/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13007-024-01236-w","type":"published","date":"2024-08-20T15:57:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63998436,"identity":"7b05960f-71c0-4e3f-b1b1-8c3f0312b92f","added_by":"auto","created_at":"2024-09-04 18:16:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2496894,"visible":true,"origin":"","legend":"","description":"","filename":"DanielSsubmission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3889721/v1_covered_c85ff2a9-5ac5-4a70-aac1-f5326f08bede.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction accuracy and heritability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location","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":"Wheat Biomass estimation, UAV based monitoring, High throughput Phenotyping, Field-Based","lastPublishedDoi":"10.21203/rs.3.rs-3889721/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3889721/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study explores the use of Unmanned Aerial Vehicles (UAVs) for estimating wheat biomass, focusing on the impact of phenotyping and analytical protocols. It emphasizes the importance of variable selection, model specificity, and sampling location within the experimental plot in predicting biomass, aiming to refine UAV-based estimation techniques for enhanced selection accuracy and throughput in variety testing and breeding programs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe research uncovered that integrating geometric and spectral traits with a partial least squares regression (PLSR) based variable selection workflow notably enhanced biomass prediction accuracy. A key finding was that models, tailored to specific maturity stages (vegetative, flowering, and grain-fill) were more accurate than those modelling the entire growth season for estimation of biomass at corresponding stages. However, experiment specific models did not significantly increase accuracy. 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