KrishiSetu: An AI-Driven Smart Agricultural Management Platform

KrishiSetu: An AI-Driven Smart Agricultural Management Platform | 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 KrishiSetu: An AI-Driven Smart Agricultural Management Platform bhagyesh, Prathamesh, mohit, vednat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8978724/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 India generates approximately 350 million tons of agricul tural waste annually, with a substantial portion being openly burned, leading to severe environmental pollution, soil degra dation, and missed economic opportunities. This paper presents KrishiSetu, an intelligent agricultural waste supply chain optimization platform that leverages artificial intelli gence, machine learning, and digital marketplace technologies to transform agricultural waste from an environmental burden into an economic asset. The proposed system integrates multi ple innovative components including an AI-powered prediction engine using Random Forest algorithms for optimal waste sell ing time forecasting, a digital auction marketplace connecting farmers directly with industries, an equipment rental facility, and an intelligent chatbot advisory system. The platform em ploys a dual-login architecture with role-based access control for farmers and businesses, ensuring secure and efficient oper ations. Experimental validation demonstrates that KrishiSetu successfully reduces open burning incidents by 67%, increases farmer income by an average of 34%, and optimizes waste-to industry conversion efficiency by 58%. The system processes multilingual government scheme information, provides real time market insights, and implements a green government schemes mechanism to encourage sustainable practices. This work represents a significant advancement in precision agricul ture and sustainable waste management, offering a scalable solution applicable to agricultural economies worldwide. agricultural waste management artificial intelligence supply chain optimization digital marketplace precision agriculture machine learning sustainable farming waste-to-wealth conversion Random Forest algorithm Full Text Additional Declarations The authors declare potential competing interests as follows: The authors declare that there are no competing interests regarding the publication of this paper. 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-8978724","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research 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