Lightweight Deep Learning Models for Efficient Classification of Plant-Parasitic Nematodes on Resource-Constrained Devices

Lightweight Deep Learning Models for Efficient Classification of Plant-Parasitic Nematodes on Resource-Constrained Devices | 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 Lightweight Deep Learning Models for Efficient Classification of Plant-Parasitic Nematodes on Resource-Constrained Devices Eyasu Saketa Terfasa, Beira Hailu Meressa, Boaz B. Tulu, Kinde Anlay Fante This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9526310/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 Agricultural productivity underpins global food security, yet it faces growing threats from plant diseases. Among these Plant Parasitic Nematodes (PPNs) contribute to an estimated annual crop loss exceeding \ $ 157 billion. Existing methods for identifying PPNs are labor‑intensive and require specialized expertise, which limits their practical application in low resource settings. To address this challenge, we developed a lightweight deep learning models tailored for PPN classification using minimal computational resources. The models were trained on a curated dataset of 1,427 microscopic images representing eight widely distributed PPN genera in Ethiopia. Among the models evaluated, EfficientNetV2B0 has achieved superior performance compared with EfficientNetB0, NASNet Mobile, and MobileNetV2. Applying additional model compression techniques, including pruning and quantization, reduced the model size to 504 KB while incurring only a minimal accuracy loss of 0.63% (from 99.3% to 98.67%). The compressed model was deployed on a Raspberry Pi 3B and demonstrated inference times between 0.26 and 0.29 seconds per image. This compact and efficient model has strong potential for battery powered agricultural devices, enhancing the usability and accessibility of real time PPN classification in resource constrained environments. By enabling accurate identification of nematode threats, our research presents a promising technological solution to enhance crop management and drive gains in agricultural productivity. Food Security Plant-Parasitic Nematodes image classification machine learning deep learning edge AI 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. 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-9526310","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630440584,"identity":"b80cbfdb-a198-4e48-aa31-51502d98ed30","order_by":0,"name":"Eyasu Saketa Terfasa","email":"","orcid":"","institution":"Jimma University","correspondingAuthor":false,"prefix":"","firstName":"Eyasu","middleName":"Saketa","lastName":"Terfasa","suffix":""},{"id":630440585,"identity":"d661aa5a-3fdb-4845-a822-22428bd55327","order_by":1,"name":"Beira Hailu Meressa","email":"","orcid":"","institution":"Jimma University","correspondingAuthor":false,"prefix":"","firstName":"Beira","middleName":"Hailu","lastName":"Meressa","suffix":""},{"id":630440588,"identity":"5daa5ba1-5721-4a09-826d-0b140bc6c9ae","order_by":2,"name":"Boaz B. 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