Advanced EfficientNetB3 based CNN for Multi-Class Plant Leaf Disease Detection

Advanced EfficientNetB3 based CNN for Multi-Class Plant Leaf Disease Detection | 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 Advanced EfficientNetB3 based CNN for Multi-Class Plant Leaf Disease Detection Bushra Kanwal, Maria Ashraf, Zakia Jalil, Tahani Alsubait This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8553348/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 In developing countries, agriculture plays an essential role in economic growth by providing food security, employment, and raw materials for industries. Diseases in plants, as in other agricultural groups, have a great impact on the reduction of global crop yields. This indicates a need for modern precision agriculture to perform their identification fast and accurately. Nowadays, Artificial Intelligence (AI) and deep-learning algorithms are used to detect diseases from leaves images. These approaches are significantly better than traditional methods. Nevertheless, visually similar diseases identification and high accuracy gains involving plenty of categories continue to be challenging. In this research, we used plant village dataset and applied an efficient and enhanced deep learning approach to classify 38 distinct plant leaf diseases. EfficientNetB3 based Convolutional Neural Network (ENBCNN) is selected as a generalizing feature extractor and produces a distinctive classification layer projecting fine patterns of disease with high discriminability to support differentiation. The results of the experiments showed great accuracy 99.7%, very stable learning curves, and reliable cross-validation effects. Our proposed method can accurately identify a wide spectrum of diseases. Therefore, developing this system, in practical way to implement it into mobile devices and increase the data to improve its usefulness are real conditions. Plant disease Agriculture Detection EfficientNetB3 CNN 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-8553348","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571600272,"identity":"e6762768-1982-400c-a759-f496e9eefc4d","order_by":0,"name":"Bushra Kanwal","email":"","orcid":"","institution":"National University of Computer and Emerging Sciences(FAST-NUCES), Islamabad 44000, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Bushra","middleName":"","lastName":"Kanwal","suffix":""},{"id":571600275,"identity":"62c593cf-0d51-40c8-a71c-f0a27997065e","order_by":1,"name":"Maria 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