Predicting Crop Disease Outbreak Based on Unstructured Farmer Complaint Text Using Nlp and Weak Supervision

Predicting Crop Disease Outbreak Based on Unstructured Farmer Complaint Text Using Nlp and Weak Supervision | 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 Predicting Crop Disease Outbreak Based on Unstructured Farmer Complaint Text Using Nlp and Weak Supervision Dr.J.Dafni Rose, keerthana mani, Razia Sulthana, Dr L Sai Ramesh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8581964/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 Crop disease reporting systems in many agricultural regions rely heavily on manual inspection[ 1 ] and expert interpretation[ 2 ] leading to delays and limited scalability. To address this challenge, this paper introduces Agri-TextPredict, a lightweight text-based framework that analyzes unstructured farmer complaint narratives to automatically predict crop disease categories and urgency levels. The system uses a Weak Supervision (WS) approach, where domain-driven labeling functions generate large volumes of probabilistic labels, significantly reducing the need for manual annotation. Preprocessed text is transformed using TF-IDF features[ 3 ][ 4 ] and classified using classic machine learning models such as Logistic Regression[ 5 ] and SVM[ 6 ]. Experimental evaluation shows that Agri-TextPredict achieves 95% accuracy, while reducing human labeling effort by 91.7% and minimizing computational requirements, making it suitable for deployment in low-resource agricultural settings. The findings demonstrate that weakly supervised NLP provides an effective and scalable solution for early disease-related decision support based solely on farmer-reported text data. Crop Disease Prediction Natural Language Processing Weak Supervision Label-Efficient Learning Early Warning System Text Analytics 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. 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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-8581964","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584790344,"identity":"9d72088b-5ee8-4ce5-b7ea-0c278073c615","order_by":0,"name":"Dr.J.Dafni Rose","email":"","orcid":"","institution":"St. Joseph's Institute of Technology","correspondingAuthor":false,"prefix":"Dr.","firstName":"J.Dafni","middleName":"","lastName":"Rose","suffix":""},{"id":584790345,"identity":"203b087f-6683-45ad-b060-4da9c33f5df0","order_by":1,"name":"keerthana mani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACCRDB2ABmMzN8AJJs7MRoOQjVwjgDpIWZFC3MPBDL8APJ9uZjnz/usMvj7z+dbGzza5s8HzMD44ePObi1SPMcS55x8ExyscSN3M3JuX23DduYGZglZ27DrUVOIseY4WAbc2LDDd7Nh3N7bjMCtbAx8+LTIv/+M1BLfeL882c3H7bsuW1PUIu0BA8zUMvhxA0HgA5j+HE7kaAWyZ40Y4azbccTNwL9YtjbcDu5jZmxGa9fJI4ffsxQ2VadOA/oMIkff27bzm9vPvjhIx4tqICxDUw2EKseBP6QongUjIJRMApGCgAA/StU+xiNqZoAAAAASUVORK5CYII=","orcid":"","institution":"St. Joseph's Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"keerthana","middleName":"","lastName":"mani","suffix":""},{"id":584790346,"identity":"bc3d3557-c3ad-4f6a-8927-753884779295","order_by":2,"name":"Razia Sulthana","email":"","orcid":"","institution":"University of Greenwich","correspondingAuthor":false,"prefix":"","firstName":"Razia","middleName":"","lastName":"Sulthana","suffix":""},{"id":584790347,"identity":"84d83967-0c21-49ef-bd7c-a1bec44a6f22","order_by":3,"name":"Dr L Sai Ramesh","email":"","orcid":"","institution":"St. Joseph's Institute of Technology","correspondingAuthor":false,"prefix":"Dr","firstName":"L","middleName":"Sai","lastName":"Ramesh","suffix":""}],"badges":[],"createdAt":"2026-01-12 12:53:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8581964/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8581964/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109164174,"identity":"b34d7ace-da0f-418c-8a79-2f1db1d4a0ec","added_by":"auto","created_at":"2026-05-13 08:02:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":530715,"visible":true,"origin":"","legend":"","description":"","filename":"cropdisease.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8581964/v1_covered_cfa7256f-472b-4b2d-b49f-658483cdcc7e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePredicting Crop Disease Outbreak Based on Unstructured Farmer Complaint Text Using Nlp and Weak Supervision\u003c/p\u003e","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":"Crop Disease Prediction, Natural Language Processing, Weak Supervision, Label-Efficient Learning, Early Warning System, Text Analytics","lastPublishedDoi":"10.21203/rs.3.rs-8581964/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8581964/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCrop disease reporting systems in many agricultural regions rely heavily on manual inspection[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and expert interpretation[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] leading to delays and limited scalability. To address this challenge, this paper introduces Agri-TextPredict, a lightweight text-based framework that analyzes unstructured farmer complaint narratives to automatically predict crop disease categories and urgency levels. The system uses a Weak Supervision (WS) approach, where domain-driven labeling functions generate large volumes of probabilistic labels, significantly reducing the need for manual annotation. Preprocessed text is transformed using TF-IDF features[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and classified using classic machine learning models such as Logistic Regression[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and SVM[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Experimental evaluation shows that Agri-TextPredict achieves 95% accuracy, while reducing human labeling effort by 91.7% and minimizing computational requirements, making it suitable for deployment in low-resource agricultural settings. 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