Estimation of Cotton Leaf Area Index under Verticillium Wilt Stress ： Based on UAV Multispectral & Optimization Algorithm

Estimation of Cotton Leaf Area Index under Verticillium Wilt Stress ： Based on UAV Multispectral & Optimization Algorithm | 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 Estimation of Cotton Leaf Area Index under Verticillium Wilt Stress ： Based on UAV Multispectral & Optimization Algorithm Yujing Tian, Jiaojiao Hui, Nannan Zhang, Mingdeng Shi, Li Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9387581/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Verticillium wilt is one of the main factors hindering cotton yield increase, the more severe the disease, the more severe the yield loss. In order to achieve early detection and prevention of Verticillium wilt, this study investigated cotton plants naturally affected by Verticillium wilt in the field. We analyzed the cotton canopy multispectral data under the stress of Verticillium wilt and the leaf area index (LAI) data collected from the ground. Due to the low prediction accu-racy of traditional empirical models, this paper selected three different back propagation BP neural network models with 5, 10, and 15 hidden layer nodes (hln) and optimized them using four intelligent swarm algorithms: genetic algorithm (GA), particle swarm optimization (PSO), spotted hyena algorithm (SHO), and improved spotted hyena algorithm (ISHO) to estimate the cotton LAI under the stress of Verticillium wilt. Finally, BP algorithm with different node number of hidden layer was optimized by comparing four intelligent swarm optimization al-gorithms to estimate LAI of cotton under verticillium wilt stress. The findings indicated that GA-BP (hln15) was the best in the GA-BP algorithm; In the PSO-BP algorithm, PSO-BP (hln10) performed best; In the SHO-BP model, SHO-BP (hln5) had the highest model performance; In the ISHO-BP model, ISHO-BP (hln5) is the best, with a determination coefficient(R2), root mean square error(RMSE), and prediction accuracy(PA) of 0.952, 0.235, and 89.30%, respec-tively; The estimation results of ISHO-BP (hln5) model can better represent the distribution of Verticillium wilt plants than GA-BP (hln15), PSO-BP (hln10), and SHO-BP (hln5), and its estimation error range is (-0.8,0.5). Therefore, the application of ISHO-BP (hln5) model pro-vides a new method for estimating cotton LAI under pest and disease stress, and also provides technical support to meet the diversified needs of cotton farmers to increase their income and national economic development. leaf area index verticillium wilt stress UAV multispectral Optimization algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 28 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Submission checks completed at journal 20 Apr, 2026 First submitted to journal 11 Apr, 2026 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-9387581","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631737410,"identity":"78206d77-666a-4326-8362-0ff7125e00ca","order_by":0,"name":"Yujing Tian","email":"","orcid":"","institution":"Tarim University","correspondingAuthor":false,"prefix":"","firstName":"Yujing","middleName":"","lastName":"Tian","suffix":""},{"id":631737411,"identity":"6e3762a1-944c-4d71-9e9e-7c64390fde37","order_by":1,"name":"Jiaojiao Hui","email":"","orcid":"","institution":"Tarim University","correspondingAuthor":false,"prefix":"","firstName":"Jiaojiao","middleName":"","lastName":"Hui","suffix":""},{"id":631737412,"identity":"5f475e4a-1517-4189-968c-2b66b94343bd","order_by":2,"name":"Nannan Zhang","email":"","orcid":"","institution":"Tarim University","correspondingAuthor":false,"prefix":"","firstName":"Nannan","middleName":"","lastName":"Zhang","suffix":""},{"id":631737413,"identity":"6dd7b3d8-f578-40e9-82db-de590a60698d","order_by":3,"name":"Mingdeng Shi","email":"","orcid":"","institution":"Tarim University","correspondingAuthor":false,"prefix":"","firstName":"Mingdeng","middleName":"","lastName":"Shi","suffix":""},{"id":631737414,"identity":"460b4b0a-5fcb-4fc4-80b3-9c9a55705516","order_by":4,"name":"Li Wang","email":"","orcid":"","institution":"Tarim University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Wang","suffix":""},{"id":631737415,"identity":"a65d3915-a27d-4cea-bb0b-df500f7adef0","order_by":5,"name":"Qingsong Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACNvbGhsN/DCR4+NkboEIHCGjh4zl88AFPhY2cZM9hIrXISaQlG/CcSTM2uJFMpBY2hhwzCcm2w4kNN98f/FzZxiDHdyOB8XMBXi1nzCQMgVoaZyczS55tYzCWvJHALD0DnxbGHjOJRKCWZulkBsnGNobEDTcS2Jh58Glh5jGTOAjU0iZ5mPknUEs9YS1sbMmGDUDv80gws4FsSTAgqIWH+eBjBmAgS/Akm1k2nJMwnHnmYbM0Pi3y8x82HGYARqX98YOPbzaU2cjzHU8++BmfFnQgAcSMDSRoGAWjYBSMglGADQAARn1KoiaEdjQAAAAASUVORK5CYII=","orcid":"","institution":"Tarim University","correspondingAuthor":true,"prefix":"","firstName":"Qingsong","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2026-04-11 11:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9387581/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9387581/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108189968,"identity":"12f8aa9d-40e0-4ed7-b715-9c7f1e06d791","added_by":"auto","created_at":"2026-04-30 09:43:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1379791,"visible":true,"origin":"","legend":"","description":"","filename":"NewManuscript1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9387581/v1_covered_06d6a88a-7afc-45d8-aa5f-587ea3c7ffd6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimation of Cotton Leaf Area Index under Verticillium Wilt Stress ： Based on UAV Multispectral \u0026 Optimization Algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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":"leaf area index, verticillium wilt stress, UAV multispectral, Optimization algorithm","lastPublishedDoi":"10.21203/rs.3.rs-9387581/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9387581/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVerticillium wilt is one of the main factors hindering cotton yield increase, the more severe the disease, the more severe the yield loss. In order to achieve early detection and prevention of Verticillium wilt, this study investigated cotton plants naturally affected by Verticillium wilt in the field. We analyzed the cotton canopy multispectral data under the stress of Verticillium wilt and the leaf area index (LAI) data collected from the ground. Due to the low prediction accu-racy of traditional empirical models, this paper selected three different back propagation BP neural network models with 5, 10, and 15 hidden layer nodes (hln) and optimized them using four intelligent swarm algorithms: genetic algorithm (GA), particle swarm optimization (PSO), spotted hyena algorithm (SHO), and improved spotted hyena algorithm (ISHO) to estimate the cotton LAI under the stress of Verticillium wilt. Finally, BP algorithm with different node number of hidden layer was optimized by comparing four intelligent swarm optimization al-gorithms to estimate LAI of cotton under verticillium wilt stress. The findings indicated that GA-BP (hln15) was the best in the GA-BP algorithm; In the PSO-BP algorithm, PSO-BP (hln10) performed best; In the SHO-BP model, SHO-BP (hln5) had the highest model performance; In the ISHO-BP model, ISHO-BP (hln5) is the best, with a determination coefficient(R2), root mean square error(RMSE), and prediction accuracy(PA) of 0.952, 0.235, and 89.30%, respec-tively; The estimation results of ISHO-BP (hln5) model can better represent the distribution of Verticillium wilt plants than GA-BP (hln15), PSO-BP (hln10), and SHO-BP (hln5), and its estimation error range is (-0.8,0.5). Therefore, the application of ISHO-BP (hln5) model pro-vides a new method for estimating cotton LAI under pest and disease stress, and also provides technical support to meet the diversified needs of cotton farmers to increase their income and national economic development.\u003c/p\u003e","manuscriptTitle":"Estimation of Cotton Leaf Area Index under Verticillium Wilt Stress ： Based on UAV Multispectral \u0026amp; Optimization Algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 09:42:00","doi":"10.21203/rs.3.rs-9387581/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"263046436173618834057866689511882456987","date":"2026-04-28T15:30:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T23:54:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T07:38:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-20T07:37:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Methods","date":"2026-04-11T11:36:31+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":"1a637342-6552-406e-8001-f9599b85983a","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T09:42:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 09:42:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9387581","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9387581","identity":"rs-9387581","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}