Forecasting Cocoon Silk Prices Using Machine Learning for Sericultural Planning

Forecasting Cocoon Silk Prices Using Machine Learning for Sericultural Planning | 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 Forecasting Cocoon Silk Prices Using Machine Learning for Sericultural Planning Vamsi Krishna Pallapati, Raju Anitha, Shiva Sai Prasad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7034979/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 Silk farming is one of the leading subsectors of agricultural practices, especially among peasants growing cocoons silk crops, they are greatly concerned with the issue on the instability of the prices of cocoons silk. Forecasting such changes in price can be quite a complex task given that things like the environmental factors, farming practices, and changes in the market, among others cause such changes. Some of these factors are not easy to incorporate in the traditional forecasting tools hence resulting to either over or under estimation which is not favourable to the silk farmers. In the present paper, there was the employment of a machine learning algorithm of Random Forest Regressor to enhance the likelihood of the price prediction. It is trained using history price data from minimum price, the maximum price and the year. As a preliminary process, the data was prepared by removing or handling any null value and putting date into the appropriate format so that the data would be coherent enough for the training process. Upon the completion of the training process, the developed model displayed very high performances such as a very low RMSE of 493.49, an R-squared of 0.99 and a high prediction accuracy of 99.21%. These figures indicate that in a way, it is possible to account for the various interaction patterns that occur between these prices. It also can help make predictions more exact and also decision-making, which would make the market of silk more stable and enhance the economic steadiness of people who are engaged in silk farming. It can be enlarged in forms that include more factors that can be available such as climate or the current market status so as to make the model more useful to the farmer, trader and the policy maker. Sericulture Cocoon Silk Price Prediction Machine Learning Random Forest 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-7034979","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":479992934,"identity":"30eb027a-d832-4cc8-97a4-00a64e6d9932","order_by":0,"name":"Vamsi Krishna Pallapati","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie3OMUvDQBTA8ScH1+XFrBcM9iscFBQxkK9yoRC/gThJJJCp7u3kh+jieOFBugQ/gYMS6OSQ4tLBwUuqTrlCNyn3h1uO97t7AC7XP8yHMtPJl8A4ZwRvANhfK3O4hQQPVOpNFoVyxKfd5A9RdiKpmpaLLI2kj5P+cfj7xlZVS/KeCS8ZfsqkeA3l6rFp37cEp2fZoDiZdaQmvMq9pUqKNcp6dSGUIuChHiRMGIKcEMhb6qQgDOYph56I4e34+OOXYLMjT2vW7iMIlSoXRYoGMtURX3AQ+4gA0rqtIwxyPpHqxRBMzXB6gzYS6zJv1Z2IfZ8a0d5SzEcV22yj6/PxfJhYy/GwedP9wcLlcrmOtm9jFmL/vBaldAAAAABJRU5ErkJggg==","orcid":"","institution":"Koneru Lakshmaiah Education Foundation","correspondingAuthor":true,"prefix":"","firstName":"Vamsi","middleName":"Krishna","lastName":"Pallapati","suffix":""},{"id":479992935,"identity":"9b3bc279-8274-4bc3-8e3a-3e4586095ae6","order_by":1,"name":"Raju Anitha","email":"","orcid":"","institution":"Koneru Lakshmaiah Education Foundation","correspondingAuthor":false,"prefix":"","firstName":"Raju","middleName":"","lastName":"Anitha","suffix":""},{"id":479992936,"identity":"a27bf19c-441a-41d8-936a-7f1a1a5faa62","order_by":2,"name":"Shiva Sai Prasad","email":"","orcid":"","institution":"Koneru Lakshmaiah Education Foundation","correspondingAuthor":false,"prefix":"","firstName":"Shiva","middleName":"Sai","lastName":"Prasad","suffix":""}],"badges":[],"createdAt":"2025-07-03 06:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7034979/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7034979/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87167672,"identity":"491d236d-e112-4e1e-be3b-f665defd2716","added_by":"auto","created_at":"2025-07-21 06:47:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":813368,"visible":true,"origin":"","legend":"","description":"","filename":"ManuScript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7034979/v1_covered_c1f46cfd-5050-4a9f-ba8a-7d67543caceb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Forecasting Cocoon Silk Prices Using Machine Learning for Sericultural Planning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Sericulture, Cocoon Silk Price Prediction, Machine Learning, Random Forest","lastPublishedDoi":"10.21203/rs.3.rs-7034979/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7034979/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSilk farming is one of the leading subsectors of agricultural practices, especially among peasants growing cocoons silk crops, they are greatly concerned with the issue on the instability of the prices of cocoons silk. Forecasting such changes in price can be quite a complex task given that things like the environmental factors, farming practices, and changes in the market, among others cause such changes. Some of these factors are not easy to incorporate in the traditional forecasting tools hence resulting to either over or under estimation which is not favourable to the silk farmers. In the present paper, there was the employment of a machine learning algorithm of Random Forest Regressor to enhance the likelihood of the price prediction. It is trained using history price data from minimum price, the maximum price and the year. As a preliminary process, the data was prepared by removing or handling any null value and putting date into the appropriate format so that the data would be coherent enough for the training process. 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