A Transfer Learning Approach to Classify InsectDiversity Based on Explainable AI

A Transfer Learning Approach to Classify InsectDiversity Based on Explainable AI | 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 A Transfer Learning Approach to Classify InsectDiversity Based on Explainable AI Md Mahmudul Hasan, SM Shaqib, Sharmin Akter, Alaya Parvin Alo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5017552/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Apr, 2025 Read the published version in Discover Life → Version 1 posted 9 You are reading this latest preprint version Abstract This paper suggests an approach to insect identification using transfer learning combined with an explainable artificial intelligence (XAI) technique. Thisstudy represents the importance of incorporating XAI to ensure the reliabilityand trustworthiness of automated insect identification systems. The proposedresearch influences the expanding body of knowledge on advanced AI techniquesfor biological classification, paving the way for future innovations in entomology, agriculture, and ecological monitoring. Transparency and interoperabilitymade possible by the integration of XAI enable a thorough comprehension ofthe decision-making process underlying the model’s predictions. The gradient-weighted class activation mapping, or Grad-CAM, approach is what we employ. Establishing transparency is crucial in fostering confidence in automated systemsand guaranteeing their pragmatic implementation in real-life situations. We proposed the ResNet152v2 model achieving a classification accuracy of 96% on acomprehensive dataset of 4,509 insect images, spanning 9 distinct classes. Thisapproach boosts model performance and reduces the dependency on extensivelabeled datasets. Transfer learning ResNet152v2 XAI Grad-CAM Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Apr, 2025 Read the published version in Discover Life → Version 1 posted Editorial decision: Revision requested 27 Sep, 2024 Reviews received at journal 26 Sep, 2024 Reviews received at journal 25 Sep, 2024 Reviewers agreed at journal 25 Sep, 2024 Reviewers agreed at journal 23 Sep, 2024 Reviewers invited by journal 23 Sep, 2024 Editor assigned by journal 20 Sep, 2024 Submission checks completed at journal 17 Sep, 2024 First submitted to journal 02 Sep, 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. 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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-5017552","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":359641362,"identity":"111cdd1c-efd8-4590-9133-e2420cfc8788","order_by":0,"name":"Md Mahmudul Hasan","email":"","orcid":"","institution":"Daffodil International University","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Mahmudul","lastName":"Hasan","suffix":""},{"id":359641363,"identity":"af65fc66-6753-4ecb-8440-a1d0f2f7254d","order_by":1,"name":"SM 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