Comparative Analysis of Conventional and Deep Learning Algorithms for Apple Detection

Comparative Analysis of Conventional and Deep Learning Algorithms for Apple 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 Comparative Analysis of Conventional and Deep Learning Algorithms for Apple Detection Hrishit Das, Ruchita K. Vehale, Pranav Shirgur This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7433552/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 Precise apple counting is one of the vital tasks in the overall context of agricultural applications, from yield estimation to resource allocation and several other logistical issues about harvesting. The paper has discussed a comparison of conventional image processing techniques with modern deep learning for the detection of apples. Apple detection research, focusing on apples under difficult orchard situations using the MinneApple dataset to ensure strong deep learning-based detection due to YOLOv8, was studied. In particular, it compared traditional approaches using HSV color segmentation and morphological operations against a fine-tuned YOLOv8 model, improved by Roboflow. These studies proved the deep learning approach of difficult scenarios with very high precision, recall, and F1 score, thus enabling it to rightly distinguish apples on the trees from those lying on the ground. Results are highly useful in understanding how traditional and modern methods can be integrated into agricultural automation. 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-7433552","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504157387,"identity":"bd4d85a2-f12f-4f50-aec0-5ac8f912bb8c","order_by":0,"name":"Hrishit Das","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Hrishit","middleName":"","lastName":"Das","suffix":""},{"id":504157388,"identity":"bdd9875f-dfa8-4807-9c94-46c263065386","order_by":1,"name":"Ruchita K. 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