Attention-Based Deep Convolutional Neural Networks for Plant Disease Classification

Attention-Based Deep Convolutional Neural Networks for Plant Disease Classification | 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 Attention-Based Deep Convolutional Neural Networks for Plant Disease Classification Sachin B. Jadhav, Pratik Pal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7463210/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 Plant diseases pose a significant threat to global food security and agricultural productivity. In this work, we propose a novel deep convolutional neural network (CNN) model enhanced with Squeeze-and-Excitation (SE) blocks and Attention Gates (AGs) for multi-class plant disease classification across five crops: apple, maize, grape, potato, and tomato. Leveraging a large image dataset and a comprehensive training regime, the proposed model achieves high performance across all metrics, including 99% accuracy, 0.99 F1-score, and strong specificity. Evaluation includes feature visualization and Grad-CAM interpretability. The model's robustness and interpretability make it a compelling solution for practical agricultural applications. Deep learning CNN plant disease classification Squeeze-and-Excitation block Attention Gate Grad-CAM precision agriculture Full Text 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-7463210","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511273377,"identity":"49bb0e89-e5be-4205-9998-64f75b6f78f4","order_by":0,"name":"Sachin B. 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