Automated Pipeline for Leaf Spot Severity Scoring in Peanuts Using Segmentation Neural Networks

Automated Pipeline for Leaf Spot Severity Scoring in Peanuts Using Segmentation Neural Networks | 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 Automated Pipeline for Leaf Spot Severity Scoring in Peanuts Using Segmentation Neural Networks Joshua Larsen, Jeffrey Dunne, Robert Austin, Cassondra Newman, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5059528/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2025 Read the published version in Plant Methods → Version 1 posted 4 You are reading this latest preprint version Abstract Background: Late and early leaf spot in peanuts is a foliar disease contributing to a significant amount of lost yield globally. Peanut breeding programs frequently focus on developing disease-resistant peanut genotypes. However, existing phenotyping protocols employ subjective rating scales, performed by human raters, who determine the severity of leaf spot infection. The objective of this study was to develop an objective end-to-end pipeline that can serve to replace an expert human scorer in the field. This was accomplished using image capture protocols and segmentation neural networks that extracted lesion areas from plot-level images to determine an appropriate rating for infection severity. Results: The pipeline incorporated a neural network that accurately determined the infected leaf surface area and identified dead leaves from plot-level cellphone imagery. Image processing algorithms then convert these labels into quality metrics that can efficiently score these images based on infected versus non-infected area. The pipeline was evaluated using field data from plots with varying leaf spot severity, creating a dataset of thousands of images that spanned conventional visual severity scores ranging from 1-9. These predictions were based on the amount of infected leaf area and the presence of defoliated leaves in the surrounding area. We were able to demonstrate automated scoring, as compared to exprt visual scoring, with a root mean square error of 0.996 visual scores, on individual images (one image per plot), and 0.800 visual scores when three images were captured of each plot. Conclusion: Results indicated that the model and image processing pipeline can serve as an alternative to human scoring. Eliminating human subjectivity for the scoring protocols will allow non-experts to collect scores and may enable drone-based data collection. This could reduce the time needed to obtain new lines or identify new genes responsible for leaf spot resistance in peanut. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Feb, 2025 Read the published version in Plant Methods → Version 1 posted Editorial decision: Revision requested 10 Sep, 2024 Editor assigned by journal 10 Sep, 2024 Submission checks completed at journal 10 Sep, 2024 First submitted to journal 09 Sep, 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. 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-5059528","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":352244513,"identity":"c5612c9d-a7cf-44a1-a178-0acb226c3853","order_by":0,"name":"Joshua Larsen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACxgbmBhAtxw8XIKwFosZYsoFYLTA1iRsOEKuFuf1g4+eKPzaMm283P/7Mw2AjC9OL246exGbJs21pzGZ3jplJ8zCkGRPWMoOxQbKx4TCb2Y0cNmYehsOJxGhp/tnw5z+P8YwcZqDD/hOlpU2yge2AhIFEDgPQYQeI0NKT2GbZ2JZsIHEjzUxyjkGy8UxCWgzbDx++2fDHrr5/RvLjD28q7GT7CGppQOEaEFAOAvJEqBkFo2AUjIKRDgAHDEOvge6OIAAAAABJRU5ErkJggg==","orcid":"","institution":"North Carolina State University","correspondingAuthor":true,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Larsen","suffix":""},{"id":352244514,"identity":"c1580f7e-0f02-42e7-a7e4-e528afbde979","order_by":1,"name":"Jeffrey Dunne","email":"","orcid":"","institution":"North Carolina State University","correspondingAuthor":false,"prefix":"","firstName":"Jeffrey","middleName":"","lastName":"Dunne","suffix":""},{"id":352244515,"identity":"ccad6e81-6fbc-4531-92b0-f37c78dee888","order_by":2,"name":"Robert Austin","email":"","orcid":"","institution":"North Carolina State University","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Austin","suffix":""},{"id":352244516,"identity":"125f9851-ebc7-45d0-94bc-7e70692886fc","order_by":3,"name":"Cassondra Newman","email":"","orcid":"","institution":"North Carolina State University","correspondingAuthor":false,"prefix":"","firstName":"Cassondra","middleName":"","lastName":"Newman","suffix":""},{"id":352244517,"identity":"e0b9661c-3d78-4e83-a319-2f2e1cf4ca22","order_by":4,"name":"Michael Kudenov","email":"","orcid":"","institution":"North Carolina State University","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Kudenov","suffix":""}],"badges":[],"createdAt":"2024-09-09 16:42:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5059528/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5059528/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13007-024-01316-x","type":"published","date":"2025-02-20T15:57:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":77052677,"identity":"bac9b33e-7016-43aa-a5cd-d124f5786547","added_by":"auto","created_at":"2025-02-24 16:23:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4882445,"visible":true,"origin":"","legend":"","description":"","filename":"LarsenPeanutScoringPipeline.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5059528/v1_covered_b4c9a198-2e80-4967-8b48-0fde4d30b01a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Automated Pipeline for Leaf Spot Severity Scoring in Peanuts Using Segmentation Neural Networks","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":"","lastPublishedDoi":"10.21203/rs.3.rs-5059528/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5059528/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Late and early leaf spot in peanuts is a foliar disease contributing to a significant amount of lost yield globally. Peanut breeding programs frequently focus on developing disease-resistant peanut genotypes. However, existing phenotyping protocols employ subjective rating scales, performed by human raters, who determine the severity of leaf spot infection. The objective of this study was to develop an objective end-to-end pipeline that can serve to replace an expert human scorer in the field. This was accomplished using image capture protocols and segmentation neural networks that extracted lesion areas from plot-level images to determine an appropriate rating for infection severity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The pipeline incorporated a neural network that accurately determined the infected leaf surface area and identified dead leaves from plot-level cellphone imagery. Image processing algorithms then convert these labels into quality metrics that can efficiently score these images based on infected versus non-infected area. The pipeline was evaluated using field data from plots with varying leaf spot severity, creating a dataset of thousands of images that spanned conventional visual severity scores ranging from 1-9. These predictions were based on the amount of infected leaf area and the presence of defoliated leaves in the surrounding area. We were able to demonstrate automated scoring, as compared to exprt visual scoring, with a root mean square error of 0.996 visual scores, on individual images (one image per plot), and 0.800 visual scores when three images were captured of each plot.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Results indicated that the model and image processing pipeline can serve as an alternative to human scoring. Eliminating human subjectivity for the scoring protocols will allow non-experts to collect scores and may enable drone-based data collection. This could reduce the time needed to obtain new lines or identify new genes responsible for leaf spot resistance in peanut.\u003c/p\u003e","manuscriptTitle":"Automated Pipeline for Leaf Spot Severity Scoring in Peanuts Using Segmentation Neural Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-15 08:48:59","doi":"10.21203/rs.3.rs-5059528/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-10T22:12:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-10T12:54:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-10T12:52:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Methods","date":"2024-09-09T16:40:50+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":"7f55bb42-28b2-49ae-af33-06e983ec268a","owner":[],"postedDate":"November 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-24T16:03:55+00:00","versionOfRecord":{"articleIdentity":"rs-5059528","link":"https://doi.org/10.1186/s13007-024-01316-x","journal":{"identity":"plant-methods","isVorOnly":false,"title":"Plant Methods"},"publishedOn":"2025-02-20 15:57:55","publishedOnDateReadable":"February 20th, 2025"},"versionCreatedAt":"2024-11-15 08:48:59","video":"","vorDoi":"10.1186/s13007-024-01316-x","vorDoiUrl":"https://doi.org/10.1186/s13007-024-01316-x","workflowStages":[]},"version":"v1","identity":"rs-5059528","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5059528","identity":"rs-5059528","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}