AI-Powered Potato Plant Disease Detection: A Vision-Language Framework

AI-Powered Potato Plant Disease Detection: A Vision-Language Framework | 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 AI-Powered Potato Plant Disease Detection: A Vision-Language Framework Sanghamitra Panda, Jayaprakash Maharana, Asfan Ali Khan, Swapnajit Sahoo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6488058/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 Potato crops are a vital part of global food security. Potato leaf and crop health play a crucial role in determining the yield and quality of potato production. This paper presents a novel approach to potato disease detection by integrating a Vision Transformer (ViT) model with a Large Language Model (LLM) for enhanced classification of potato plant diseases. We developed a multi-modal pipeline that not only accurately identifies diseases affecting potato leaves and tubers but also provides contextual explanations for the diagnoses. Experimental results demonstrate that our integrated approach outperforms traditional individual models, with the potato leaf disease classifier achieving 99.44% validation accuracy and the potato tuber disease classifier reaching 76.19% accuracy when trained separately, while the combined model maintains excellent performance of 95.06% on the validation set. The fusion of computer vision with Mistral AI's LLM capabilities creates an interpretable system that can assist agricultural experts with both disease identification and recommended treatment actions. This paper contributes to the growing field of AI-assisted agriculture by demonstrating how multi-modal deep learning systems can provide more comprehensive solutions to potato disease management challenges, potentially reducing crop losses and improving food security. Potato Disease Detection Vision Transformer Large Language Models Multi-modal AI Computer Vision Deep Learning Plant Pathology Agricultural Artificial Intelligence Disease Classification Explainable AI 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-6488058","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477799660,"identity":"d34d27a3-3a73-43c7-9416-73287f278949","order_by":0,"name":"Sanghamitra 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