Rapid Detection of Soybean Nutrient Deficiencies Using YOLOv8s: Advancing Precision Agriculture

Rapid Detection of Soybean Nutrient Deficiencies Using YOLOv8s: Advancing Precision Agriculture | 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 Article Rapid Detection of Soybean Nutrient Deficiencies Using YOLOv8s: Advancing Precision Agriculture Minsoo Jeong, Sihyun Park, Sook-Min Kwon, KyeongMo Lim, Da-Ryung Jung, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5300628/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Early detection of nutrient deficiencies is crucial for optimizing crop yields and ensuring sustainable agricultural practices. This study presents a novel application of the YOLOv8s object detection model for identifying nitrogen, phosphorus, and potassium deficiencies in soybean plants. Employing a unique dataset from a long-term nutrient-deficient field maintained for over 40 years, we trained and evaluated the model on 6,020 red, green, and blue images of soybean leaves exhibiting nutrient stress conditions. The YOLOv8s model achieved exceptional performance, with a mean average precision ( [email protected] ) of 99.18% during training and 98.51% for validation. Precision rates for individual nutrient deficiencies ranged from 90.03–96.54%, with highly accurate potassium deficiency detection. The model demonstrated robust generalization across diverse field conditions, processing images in 3.46 ms each, making it suitable for real-time applications. This research significantly advances the field of precision agriculture by providing a fast, accurate, and scalable method for detecting early nutrient deficiency in soybean crops, potentially revolutionizing fertilizer management practices and contributing to more sustainable farming systems. Biological sciences/Biotechnology Biological sciences/Computational biology and bioinformatics Biological sciences/Plant sciences Earth and environmental sciences/Environmental sciences Artificial intelligence nutrient deficiency soybeans smart farming YOLOv8 Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarymetarialsubmission.pdf Cite Share Download PDF Status: Published Journal Publication published 21 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 26 Nov, 2024 Reviews received at journal 25 Nov, 2024 Reviewers agreed at journal 24 Nov, 2024 Reviews received at journal 28 Oct, 2024 Reviewers agreed at journal 25 Oct, 2024 Reviewers invited by journal 23 Oct, 2024 Editor assigned by journal 22 Oct, 2024 Editor invited by journal 22 Oct, 2024 Submission checks completed at journal 21 Oct, 2024 First submitted to journal 20 Oct, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5300628","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":371317719,"identity":"85ea69cf-38f7-4dad-8a2a-4613e630c0e4","order_by":0,"name":"Minsoo Jeong","email":"","orcid":"","institution":"Department of Applied Biosciences, Kyungpook National University","correspondingAuthor":false,"prefix":"","firstName":"Minsoo","middleName":"","lastName":"Jeong","suffix":""},{"id":371317720,"identity":"dcd76ffb-8e4e-45d0-8e25-9a279c1bf279","order_by":1,"name":"Sihyun Park","email":"","orcid":"","institution":"Department of Integrative 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