Disorder Severity Classification in Tomato Based on Weight Cluster Loss and Convolutional Neural Network

Disorder Severity Classification in Tomato Based on Weight Cluster Loss and Convolutional Neural Network | 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 Disorder Severity Classification in Tomato Based on Weight Cluster Loss and Convolutional Neural Network Babatunde Sunday, Sahabi A. YUSUF, Mohammed Abdullahi, Yazeed Abdullahi Masha, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6641648/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Agricultural productivity is a crucial determinant of economic stability. Within the agricultural sector, particularly in tomato production, the impact of plant diseases and pests poses a significant challenge. Detecting the severity of disorders in tomato plants is essential for addressing these challenges. Achieving accurate and rapid detection is imperative for developing early treatment strategies, ultimately minimizing economic losses. While various researchers have explored solutions using convolutional neural network (CNN) models to identify and classify disease severity in tomatoes, the limited availability of training data has led to overfitting issues and inter-class similarity, resulting in suboptimal performance measures. To address the overfitting problem arising from insufficient data, this research proposes a deep transfer-based framework. Three CNN models i.e., AlexNet, SqueezeNet, and InceptionV3 are employed to classify disease severity in tomato plants, specifically targeting tomato late blight, tomato early blight, tomato leaf mold, tomato bacteria spot, and healthy tomato leaves using the PlantVillage dataset. The study incorporates a weighted-cluster loss function to mitigate inter-class similarities. Computational accuracy serves as the performance metric. Following experimentation, InceptionV3 demonstrated the highest classification accuracy at 93.66%, surpassing AlexNet (83.03%) and SqueezeNet (80.09%). Consequently, the proposed system functions as a decision support tool for farmers, aiding in the identification of disorder severity in tomato plant leaves. Tomato Disorder Severity Classification Convolutional Neural Networks (CNN) Inter-Class Similarities Overfitting Weight Cluster Loss Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 May, 2025 Reviews received at journal 28 May, 2025 Reviews received at journal 20 May, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers invited by journal 19 May, 2025 Editor assigned by journal 15 May, 2025 Submission checks completed at journal 15 May, 2025 First submitted to journal 11 May, 2025 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-6641648","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458481982,"identity":"3c49cff9-48b8-4029-915a-e04e4df81c29","order_by":0,"name":"Babatunde Sunday","email":"data:image/png;base64,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","orcid":"","institution":"Ahmadu Bello University","correspondingAuthor":true,"prefix":"","firstName":"Babatunde","middleName":"","lastName":"Sunday","suffix":""},{"id":458481984,"identity":"6dbe8813-85a9-4e18-891e-5cf1625df394","order_by":1,"name":"Sahabi A. 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