DVTXAI: A Novel Deep Vision Transformer with an Explainable AI-based Framework and its Application in Agriculture

DVTXAI: A Novel Deep Vision Transformer with an Explainable AI-based Framework and its Application in 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 Research Article DVTXAI: A Novel Deep Vision Transformer with an Explainable AI-based Framework and its Application in Agriculture Sadia Kamal, Parth Sharma, P.K. Gupta, Mohammad Khubeb Siddiqui, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4752298/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Agriculture is one of the fundamental components of human civilization, contributing not only to food production but also to economic growth. Early identification of diseases in plants presents a significant challenge, as timely detection is crucial to increasing agricultural production. Incorporating the latest technology, such as artificial intelligence (AI) techniques, in fields can reduce major losses for farmers and also improve productivity. In this paper, we have proposed a Deep Vision transformer and an Explainable AI-based technique to overcome the various diseases in plants. Here, we have used the dataset "Plant Village," as a case study that focuses on two majorly grown crops like: potatoes and tomatoes. Further, we have analyzed nine diseases that affect these crops around the globe, with tomatoes showing six different conditions and potatoes affected by three bacterial diseases. The proposed DVTXAI framework incorporates the Deep vision transforms to detect plant diseases at an early stage. In the experiments, the proposed model achieves an accuracy of 93. 56% for tomatoes and 99.95% for potatoes. Additionally, the model is Explainable AI (XAI), which reveals the transparency mechanism that helps the farmer to make the right decision at an early stage of the crop. AI in agriculture Explainable AI Plant disease detection transformer learning SHAP Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Sep, 2024 Reviews received at journal 08 Sep, 2024 Reviewers agreed at journal 23 Aug, 2024 Reviewers agreed at journal 22 Aug, 2024 Reviews received at journal 22 Aug, 2024 Reviewers agreed at journal 21 Aug, 2024 Reviewers agreed at journal 21 Aug, 2024 Reviewers agreed at journal 21 Aug, 2024 Reviewers invited by journal 21 Aug, 2024 Editor assigned by journal 18 Jul, 2024 Submission checks completed at journal 18 Jul, 2024 First submitted to journal 16 Jul, 2024 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. 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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-4752298","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":332027877,"identity":"65a930f1-8d8e-474c-9956-7f68c0e246d3","order_by":0,"name":"Sadia Kamal","email":"","orcid":"","institution":"University of Maryland, Baltimore County","correspondingAuthor":false,"prefix":"","firstName":"Sadia","middleName":"","lastName":"Kamal","suffix":""},{"id":332027880,"identity":"b7c17cc8-a43e-4084-b0fa-462ca36a4835","order_by":1,"name":"Parth Sharma","email":"","orcid":"","institution":"Jaypee University of Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Parth","middleName":"","lastName":"Sharma","suffix":""},{"id":332027882,"identity":"7476c9f4-82d8-461c-8c75-32426ccb5e21","order_by":2,"name":"P.K. 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