Machine Learning-Based Identification of Root Knot Nematodes: A Novel Paradigm in Precision Agriculture

Machine Learning-Based Identification of Root Knot Nematodes: A Novel Paradigm in Precision Agriculture | 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 Article Machine Learning-Based Identification of Root Knot Nematodes: A Novel Paradigm in Precision Agriculture Sristi Das, Koyel Ghosh, Sandip Mondal, Suvasri Dutta, Matiyar Rahaman Khan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7458387/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract The use of machine learning is an emerging approach in precision agriculture, including pest identification, disease diagnostics, and soil health monitoring. Root-knot nematodes ( Meloidogyne spp.) are a key group of soil-dwelling metazoans that cause plant diseases and lead to significant yield losses in major crops. Identification of these nematodes is essential for pest management. Manual identification of large samples is time-consuming, labour-intensive, and subject to inter- and intra-rater variations, which may affect the consistency and reliability of results. Moreover, the number of skilled taxonomists is on the decline. Automating the identification process can enhance accuracy and consistency while significantly reducing the time required for sample identification. In this study, a deep-learning architecture was developed using convolutional neural networks (CNNs) to identify three major root-knot nematode species: Meloidogyne graminicola , M. incognita , and M. javanica , which are among the most economically damaging plant-parasitic nematodes, causing significant yield losses in major crops worldwide. The algorithm, based on AlexNet and VGG16 architectures, achieved an accuracy of ~ 95%. In contrast, manual annotation by three independent annotators yielded moderate agreement (Kappa = 0.56), underscoring the challenges of inter-rater variability in large-scale nematode identification. Integrated Gradients analysis revealed that the model used taxonomically relevant features of the perineal pattern images during classification. The model also showed stable training behaviour across cross-validation folds, with minimal signs of overfitting. The machine learning model demonstrated improved reliability and precision in nematode identification. By addressing the challenge of accurate taxonomic classification, especially for non-experts, this approach offers a new paradigm for rapid and consistent identification of nematodes, essential for large-scale deployment of diagnostics and precision management of plant-parasitic nematodes. Biological sciences/Computational biology and bioinformatics Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Plant sciences Machine learning nematode identification root-knot nematode CNN AlexNet Full Text Additional Declarations No competing interests reported. Supplementary Files SupportingInformationV7.docx Cite Share Download PDF Status: Published Journal Publication published 08 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 30 Oct, 2025 Reviews received at journal 29 Oct, 2025 Reviews received at journal 27 Oct, 2025 Reviewers agreed at journal 25 Oct, 2025 Reviews received at journal 23 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers invited by journal 25 Sep, 2025 Editor assigned by journal 02 Sep, 2025 Editor invited by journal 02 Sep, 2025 Submission checks completed at journal 28 Aug, 2025 First submitted to journal 28 Aug, 2025 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-7458387","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":524841143,"identity":"305d249d-2807-4bd8-9aae-1e59554057b1","order_by":0,"name":"Sristi Das","email":"","orcid":"","institution":"Indian Statistical Institute","correspondingAuthor":false,"prefix":"","firstName":"Sristi","middleName":"","lastName":"Das","suffix":""},{"id":524841145,"identity":"32467480-df01-4361-9eff-c2f1a9b965fb","order_by":1,"name":"Koyel Ghosh","email":"","orcid":"","institution":"Indian Statistical 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identification, root-knot nematode, CNN, AlexNet","lastPublishedDoi":"10.21203/rs.3.rs-7458387/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7458387/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe use of machine learning is an emerging approach in precision agriculture, including pest identification, disease diagnostics, and soil health monitoring. Root-knot nematodes (\u003cem\u003eMeloidogyne\u003c/em\u003e spp.) are a key group of soil-dwelling metazoans that cause plant diseases and lead to significant yield losses in major crops. Identification of these nematodes is essential for pest management. Manual identification of large samples is time-consuming, labour-intensive, and subject to inter- and intra-rater variations, which may affect the consistency and reliability of results. Moreover, the number of skilled taxonomists is on the decline. Automating the identification process can enhance accuracy and consistency while significantly reducing the time required for sample identification.\u003c/p\u003e\u003cp\u003eIn this study, a deep-learning architecture was developed using convolutional neural networks (CNNs) to identify three major root-knot nematode species: \u003cem\u003eMeloidogyne graminicola\u003c/em\u003e, \u003cem\u003eM. incognita\u003c/em\u003e, and \u003cem\u003eM. javanica\u003c/em\u003e, which are among the most economically damaging plant-parasitic nematodes, causing significant yield losses in major crops worldwide. The algorithm, based on AlexNet and VGG16 architectures, achieved an accuracy of ~\u0026thinsp;95%. In contrast, manual annotation by three independent annotators yielded moderate agreement (Kappa\u0026thinsp;=\u0026thinsp;0.56), underscoring the challenges of inter-rater variability in large-scale nematode identification. Integrated Gradients analysis revealed that the model used taxonomically relevant features of the perineal pattern images during classification. The model also showed stable training behaviour across cross-validation folds, with minimal signs of overfitting.\u003c/p\u003e\u003cp\u003eThe machine learning model demonstrated improved reliability and precision in nematode identification. By addressing the challenge of accurate taxonomic classification, especially for non-experts, this approach offers a new paradigm for rapid and consistent identification of nematodes, essential for large-scale deployment of diagnostics and precision management of plant-parasitic nematodes.\u003c/p\u003e","manuscriptTitle":"Machine Learning-Based Identification of Root Knot Nematodes: A Novel Paradigm in Precision Agriculture","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 16:38:21","doi":"10.21203/rs.3.rs-7458387/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-30T08:01:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-29T22:37:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-27T20:45:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71028476689899214430811324749703971780","date":"2025-10-25T13:35:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-23T16:02:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313851438385267700016386635615942832115","date":"2025-10-22T02:18:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180734653599655183979929181342394239084","date":"2025-10-10T11:38:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172963589855338701943957284883429891696","date":"2025-09-30T08:50:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-25T08:19:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-02T06:42:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-02T05:32:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-28T10:29:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-28T10:25:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"da758404-184e-4172-bec5-b1d8f3df8201","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55782929,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":55782930,"name":"Biological sciences/Ecology"},{"id":55782931,"name":"Earth and environmental sciences/Ecology"},{"id":55782932,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-01-12T16:02:37+00:00","versionOfRecord":{"articleIdentity":"rs-7458387","link":"https://doi.org/10.1038/s41598-025-33143-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-08 15:57:13","publishedOnDateReadable":"January 8th, 2026"},"versionCreatedAt":"2025-10-07 16:38:21","video":"","vorDoi":"10.1038/s41598-025-33143-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-33143-y","workflowStages":[]},"version":"v1","identity":"rs-7458387","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7458387","identity":"rs-7458387","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}