Assessing indicators of the risk of poor pasture persistence using ground- based and airborne cameras

Assessing indicators of the risk of poor pasture persistence using ground- based and airborne cameras | 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 Assessing indicators of the risk of poor pasture persistence using ground- based and airborne cameras Chinthaka Jayasinghe, Anna Thomson, Chung Nan Hsiao, Khageswor Giri, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9309846/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 Pasture persistence supports the profitability of temperate, pasture-based livestock systems; however, early signs of decline are difficult to detect because conventional field scoring is labour and time-intensive, and current sensing approaches are not yet routinely deployed at paddock scale with sufficient sensitivity and interpretability to support timely intervention. This study evaluated the feasibility of UAV-based thermal–multispectral and ground-based hyperspectral imaging to quantify pasture indicators linked with persistence risk in ryegrass swards. Thermal imagery of a ryegrass field trial was acquired using a thermal sensor mounted on a UAV at flying heights of 20 m and 50 m during the morning, midday, and afternoon in summer across three days, each of which recorded maximum daily temperatures of ≥ 25°C and was calibrated against canopy temperature measurements obtained with a handheld infrared thermometer and in-field temperature reference targets. Orthomosaic multispectral images were radiometrically calibrated and used to derive vegetation indices that were analysed by multi-threshold segmentation for fractional cover estimation. Proximal hyperspectral images collected were classified using the Spectral Angle Mapper algorithm to classify the images into green, senescent, and soil fractions and were validated against SPAD chlorophyll measurements, visual ground-cover assessments, and dry-weight ranking estimates of pasture fractions. Thermal readings from the integrated thermal-multispectral sensor showed strong linear agreement with handheld measurements at both flying heights (R² = 0.90, LCCC = 0.82 at 20 m; R² = 0.89, LCCC = 0.81 at 50 m), while Bland–Altman diagnostics indicated proportional bias and demonstrated sensitivity to flight altitude and time of day. After accounting for weed proportion in fractional cover measurements, higher summer canopy temperature was associated with lower autumn green canopy area, confirming the use of canopy temperature as an early indicator of subsequent persistence risk. Hyperspectral estimates captured field variation in senescence (R² = 0.69; LCCC = 0.81) and ground cover (R² = 0.84; LCCC = 0.79), whereas multispectral indices were strongly associated with greenness and soil exposure (R² = 0.75; LCCC = 0.76). Overall, the results demonstrate that integrated thermal, multispectral, and hyperspectral sensing can provide scalable indicators for paddock-scale early warning of poor ryegrass persistence. However, further work is needed to overcome operational challenges, such as cross-sensor harmonisation and improved spectral discrimination between pasture and co-occurring weed species, to enhance pasture persistence monitoring capability. UAV-based Imaging Pasture persistence Moisture Stress Detection Precision Agriculture Remote Sensing Pasture Indicators Full Text Additional Declarations No competing interests reported. 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. 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-9309846","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627104612,"identity":"2e82a78d-78c1-442b-b91a-7158cc2556fb","order_by":0,"name":"Chinthaka Jayasinghe","email":"data:image/png;base64,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","orcid":"","institution":"Agriculture Science \u0026 Technology, Hamilton SmartFarm","correspondingAuthor":true,"prefix":"","firstName":"Chinthaka","middleName":"","lastName":"Jayasinghe","suffix":""},{"id":627104613,"identity":"6c93317f-7e8f-4ddf-bf35-67a0ee93df66","order_by":1,"name":"Anna Thomson","email":"","orcid":"","institution":"Agriculture Science \u0026 Technology, Ellinbank SmartFarm","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Thomson","suffix":""},{"id":627104614,"identity":"8228744b-d4f4-4fa1-81c4-6008e171067d","order_by":2,"name":"Chung Nan Hsiao","email":"","orcid":"","institution":"The University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Chung","middleName":"Nan","lastName":"Hsiao","suffix":""},{"id":627104615,"identity":"6fdac9c9-9c5b-49f9-bb5f-6061d6b52c3c","order_by":3,"name":"Khageswor Giri","email":"","orcid":"","institution":"Agriculture Science \u0026 Technology, AgriBio","correspondingAuthor":false,"prefix":"","firstName":"Khageswor","middleName":"","lastName":"Giri","suffix":""},{"id":627104616,"identity":"976f15a5-dc10-4403-a989-340fe58d61c1","order_by":4,"name":"Fan Shi","email":"","orcid":"","institution":"Agriculture Science \u0026 Technology, AgriBio","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Shi","suffix":""},{"id":627104617,"identity":"e81aa97f-a2d9-4bbe-a977-79092849639b","order_by":5,"name":"Kevin Smith","email":"","orcid":"","institution":"Agriculture Science \u0026 Technology, Hamilton SmartFarm","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"","lastName":"Smith","suffix":""},{"id":627104618,"identity":"23f23f98-4cf7-4342-abd8-d3c988b8ef0a","order_by":6,"name":"Noel Cogan","email":"","orcid":"","institution":"Agriculture Science \u0026 Technology, AgriBio","correspondingAuthor":false,"prefix":"","firstName":"Noel","middleName":"","lastName":"Cogan","suffix":""},{"id":627104619,"identity":"717a9eb6-4076-49a9-9adb-00da30649d7f","order_by":7,"name":"Joe Jacobs","email":"","orcid":"","institution":"Agriculture Science \u0026 Technology, Ellinbank SmartFarm","correspondingAuthor":false,"prefix":"","firstName":"Joe","middleName":"","lastName":"Jacobs","suffix":""}],"badges":[],"createdAt":"2026-04-03 07:09:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9309846/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9309846/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108182393,"identity":"015a2bab-1e0b-4352-8796-1fd50c8ff2b6","added_by":"auto","created_at":"2026-04-30 08:59:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1304404,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript6Final.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9309846/v1_covered_240ee2c9-b153-43bd-9d56-ec49e40bf1ac.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing indicators of the risk of poor pasture persistence using ground- based and airborne cameras","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","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},"keywords":"UAV-based Imaging, Pasture persistence, Moisture Stress Detection, Precision Agriculture, Remote Sensing, Pasture Indicators","lastPublishedDoi":"10.21203/rs.3.rs-9309846/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9309846/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePasture persistence supports the profitability of temperate, pasture-based livestock systems; however, early signs of decline are difficult to detect because conventional field scoring is labour and time-intensive, and current sensing approaches are not yet routinely deployed at paddock scale with sufficient sensitivity and interpretability to support timely intervention. This study evaluated the feasibility of UAV-based thermal\u0026ndash;multispectral and ground-based hyperspectral imaging to quantify pasture indicators linked with persistence risk in ryegrass swards. Thermal imagery of a ryegrass field trial was acquired using a thermal sensor mounted on a UAV at flying heights of 20 m and 50 m during the morning, midday, and afternoon in summer across three days, each of which recorded maximum daily temperatures of \u0026ge;\u0026thinsp;25\u0026deg;C and was calibrated against canopy temperature measurements obtained with a handheld infrared thermometer and in-field temperature reference targets. Orthomosaic multispectral images were radiometrically calibrated and used to derive vegetation indices that were analysed by multi-threshold segmentation for fractional cover estimation. Proximal hyperspectral images collected were classified using the Spectral Angle Mapper algorithm to classify the images into green, senescent, and soil fractions and were validated against SPAD chlorophyll measurements, visual ground-cover assessments, and dry-weight ranking estimates of pasture fractions. Thermal readings from the integrated thermal-multispectral sensor showed strong linear agreement with handheld measurements at both flying heights (R\u0026sup2; = 0.90, LCCC\u0026thinsp;=\u0026thinsp;0.82 at 20 m; R\u0026sup2; = 0.89, LCCC\u0026thinsp;=\u0026thinsp;0.81 at 50 m), while Bland\u0026ndash;Altman diagnostics indicated proportional bias and demonstrated sensitivity to flight altitude and time of day. After accounting for weed proportion in fractional cover measurements, higher summer canopy temperature was associated with lower autumn green canopy area, confirming the use of canopy temperature as an early indicator of subsequent persistence risk. Hyperspectral estimates captured field variation in senescence (R\u0026sup2; = 0.69; LCCC\u0026thinsp;=\u0026thinsp;0.81) and ground cover (R\u0026sup2; = 0.84; LCCC\u0026thinsp;=\u0026thinsp;0.79), whereas multispectral indices were strongly associated with greenness and soil exposure (R\u0026sup2; = 0.75; LCCC\u0026thinsp;=\u0026thinsp;0.76). Overall, the results demonstrate that integrated thermal, multispectral, and hyperspectral sensing can provide scalable indicators for paddock-scale early warning of poor ryegrass persistence. However, further work is needed to overcome operational challenges, such as cross-sensor harmonisation and improved spectral discrimination between pasture and co-occurring weed species, to enhance pasture persistence monitoring capability.\u003c/p\u003e","manuscriptTitle":"Assessing indicators of the risk of poor pasture persistence using ground- based and airborne cameras","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 15:46:24","doi":"10.21203/rs.3.rs-9309846/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"223347cf-79c0-4dab-a5c3-02443ca8013b","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"108090990226698521253491907413798195612","date":"2026-05-18T02:33:18+00:00","index":20,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T15:46:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 15:46:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9309846","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9309846","identity":"rs-9309846","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}