{"paper_id":"00e48e6a-785b-40da-b078-bc4ef9ecf352","body_text":"Application of Unmanned Aircraft Systems (UASs) for Disease Assessment and High Throughput Field Phenotyping of Plant Breeding Trials | 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 Application of Unmanned Aircraft Systems (UASs) for Disease Assessment and High Throughput Field Phenotyping of Plant Breeding Trials Tadesse Anberbir, Felix Bankole, Girma Mamo, Alemu Dabi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8436422/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 Conventional plant phenotyping relies on visual scoring and manual measurements, which are labor-intensive, time-consuming, and prone to human error. To address these limitations, Unmanned Aircraft Systems (UASs) are increasingly being applied in breeding trials to capture various phenotypic traits. High Throughput Phenotyping (HTP) offers enhanced speed, accuracy, and efficiency, while potentially reducing costs in plant breeding programs. This study explores UAS-based phenotyping in wheat breeding trials with the aim to integrate HTP platforms across breeding pipelines. UAS images were acquired using a Parrot Bluegrass drone equipped with a sequoia multispectral sensor, processed via Agisoft Metashape (open-source) and Pix4D mapper (licensed), and analyzed using PlotPhenix (licensed) for Vegetation Indices (VIs) and plot segmentation. Comparisons between UAS-based and ground-based measurements revealed that grain yield is significantly negatively correlated (r = -0.74) with yellow rust disease severity. Multispectral-derived indices, particularly **Red, Red Edge, and NIR bands, showed positive correlations with grain yield (ranging from 0.22 to 0.23), though RGB-generated indices exhibited stronger correlations. The findings confirm that UAS-generated indices effectively assess yellow rust disease severity and predict grain yield. UAS-based phenotyping enhances efficiency and accuracy in trait collection and disease assessment, facilitating the development of improved wheat varieties and promoting the integration of UAS technologies into breeding programs. Bioinformatics Remote Sensing UASs Wireless Communication Precision Agriculture Transportation Expert Systems Predictive Analytics 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. 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Disease Assessment and High Throughput Field Phenotyping of Plant Breeding Trials\\u003c/p\\u003e\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"HarpDome Technologies\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research 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To address these limitations, Unmanned Aircraft Systems (UASs) are increasingly being applied in breeding trials to capture various phenotypic traits. High Throughput Phenotyping (HTP) offers enhanced speed, accuracy, and efficiency, while potentially reducing costs in plant breeding programs.\\u003c/p\\u003e \\u003cp\\u003eThis study explores UAS-based phenotyping in wheat breeding trials with the aim to integrate HTP platforms across breeding pipelines. UAS images were acquired using a Parrot Bluegrass drone equipped with a sequoia multispectral sensor, processed via Agisoft Metashape (open-source) and Pix4D mapper (licensed), and analyzed using PlotPhenix (licensed) for Vegetation Indices (VIs) and plot segmentation.\\u003c/p\\u003e \\u003cp\\u003eComparisons between UAS-based and ground-based measurements revealed that grain yield is significantly negatively correlated (r = -0.74) with yellow rust disease severity. Multispectral-derived indices, particularly **Red, Red Edge, and NIR bands, showed positive correlations with grain yield (ranging from 0.22 to 0.23), though RGB-generated indices exhibited stronger correlations.\\u003c/p\\u003e \\u003cp\\u003eThe findings confirm that UAS-generated indices effectively assess yellow rust disease severity and predict grain yield. UAS-based phenotyping enhances efficiency and accuracy in trait collection and disease assessment, facilitating the development of improved wheat varieties and promoting the integration of UAS technologies into breeding programs.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Application of Unmanned Aircraft Systems (UASs) for Disease Assessment and High Throughput Field Phenotyping of Plant Breeding Trials\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-12-29 14:46:00\",\"doi\":\"10.21203/rs.3.rs-8436422/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"d777bcdd-4ae8-4e34-a564-840cace262dc\",\"owner\":[],\"postedDate\":\"December 29th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":60139548,\"name\":\"Bioinformatics\"}],\"tags\":[],\"updatedAt\":\"2025-12-29T14:46:00+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-12-29 14:46:00\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8436422\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8436422\",\"identity\":\"rs-8436422\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}