Machine Vision–Based Deep Learning for Automated Crop Disease Classification in Precision Agriculture Article

Machine Vision–Based Deep Learning for Automated Crop Disease Classification in Precision Agriculture Article | 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 Machine Vision–Based Deep Learning for Automated Crop Disease Classification in Precision Agriculture Article Kaznah Alshammari, Muhammad Sheraz Khan, Kainat Nisa, Irshad Ahmad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8761764/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Cherry is widely cultivated but remains challenging to harvest due to economic and ecological constraints, especially in developing countries such as Pakistan. Climate change, limited use of technology, and foliar diseases worsened by pesticide use further reduce productivity, particularly during fruiting. Conventional disease assessment depends on expert observation and grower experience, making it subjective and time-consuming. A comprehensive evaluation was conducted on the PlantCity dataset, which contains 5,714 high-density, full-color RGB images collected under challenging conditions and categorized into 5 classes. We compared three approaches: deep learning pre-trained, transfer learning, and a machine learning pipeline. Models were evaluated by accuracy, precision, recall, F1-score, Cohen’s Kappa, inference time, FLOPs, and throughput. Grad-CAM was used to improve interpretability. Transfer learning using DenseNet169 achieved the highest performance, with 99.80% accuracy, 99.80% precision, 99.80% recall, and a Cohen’s Kappa of 99.74%. These results were significantly higher than those obtained by other deep learning architectures and handcrafted baselines. Grad-CAM heatmaps confirmed that the models focused their attention on pathological areas. The proposed transfer-learning-based framework, particularly DenseNet169, demonstrates state-of-the-art diagnostic accuracy and features a modular structure. This design enables deployment on both high-performance servers and resource-constrained embedded devices, thereby facilitating early disease detection in precision agriculture. Biological sciences/Computational biology and bioinformatics Biological sciences/Ecology Earth and environmental sciences/Ecology Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Biological sciences/Plant sciences Precision Agriculture Machine Learning Deep Learning Artificial Intelligence Machine Vision Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 03 Mar, 2026 Editor invited by journal 04 Feb, 2026 Editor assigned by journal 03 Feb, 2026 Submission checks completed at journal 03 Feb, 2026 First submitted to journal 02 Feb, 2026 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-8761764","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":599853323,"identity":"79929a4f-722e-404b-8ca2-f38030f600d0","order_by":0,"name":"Kaznah Alshammari","email":"","orcid":"","institution":"Northern Border University","correspondingAuthor":false,"prefix":"","firstName":"Kaznah","middleName":"","lastName":"Alshammari","suffix":""},{"id":599853324,"identity":"f8b04eb5-c63a-462d-9e39-0ca9aac3066d","order_by":1,"name":"Muhammad Sheraz Khan","email":"","orcid":"","institution":"Islamia College University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Sheraz","lastName":"Khan","suffix":""},{"id":599853325,"identity":"a3f4a0eb-1342-41a9-882f-4315a4399494","order_by":2,"name":"Kainat Nisa","email":"","orcid":"","institution":"Islamia College University","correspondingAuthor":false,"prefix":"","firstName":"Kainat","middleName":"","lastName":"Nisa","suffix":""},{"id":599853326,"identity":"0553d508-f4eb-46fb-99fc-52f8e7a23890","order_by":3,"name":"Irshad Ahmad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBACNgiy4QHzQKQEkVrSSNAC1XWYgXgtfNLHnz34UXZehn92A+ODt20MeZINhKzgyzE37Dl3m0fizgFmw7ltDMXSBF3Fw8Mmwdt2m8dAIoFNmreNIXEeYS3szyT/tp0DaWH/TaQWBjOg4QfAtjCDtMwmwmFm0jLnkoF+OdgsOeecRDFB78v3AB32pszOnn9288EPb8ps8iQOELIGDiQYQcZLJBCtAR6FpGgZBaNgFIyCEQIAxH4ytqU0E9QAAAAASUVORK5CYII=","orcid":"","institution":"Islamia College University","correspondingAuthor":true,"prefix":"","firstName":"Irshad","middleName":"","lastName":"Ahmad","suffix":""}],"badges":[],"createdAt":"2026-02-02 07:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8761764/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8761764/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104403063,"identity":"3bbcea56-819c-47d9-b5eb-2be5eaa6aa6a","added_by":"auto","created_at":"2026-03-11 12:17:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1259143,"visible":true,"origin":"","legend":"","description":"","filename":"Paper1ScientificReport.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8761764/v1_covered_bd6cb783-f8d3-4677-b5d0-2a907c26901f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Vision–Based Deep Learning for Automated Crop Disease Classification in Precision Agriculture Article","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Precision Agriculture, Machine Learning, Deep Learning, Artificial Intelligence, Machine Vision","lastPublishedDoi":"10.21203/rs.3.rs-8761764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8761764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCherry is widely cultivated but remains challenging to harvest due to economic and ecological constraints, especially in developing countries such as Pakistan. Climate change, limited use of technology, and foliar diseases worsened by pesticide use further reduce productivity, particularly during fruiting. Conventional disease assessment depends on expert observation and grower experience, making it subjective and time-consuming. A comprehensive evaluation was conducted on the PlantCity dataset, which contains 5,714 high-density, full-color RGB images collected under challenging conditions and categorized into 5 classes. We compared three approaches: deep learning pre-trained, transfer learning, and a machine learning pipeline. Models were evaluated by accuracy, precision, recall, F1-score, Cohen’s Kappa, inference time, FLOPs, and throughput. Grad-CAM was used to improve interpretability. Transfer learning using DenseNet169 achieved the highest performance, with 99.80% accuracy, 99.80% precision, 99.80% recall, and a Cohen’s Kappa of 99.74%. These results were significantly higher than those obtained by other deep learning architectures and handcrafted baselines. Grad-CAM heatmaps confirmed that the models focused their attention on pathological areas. The proposed transfer-learning-based framework, particularly DenseNet169, demonstrates state-of-the-art diagnostic accuracy and features a modular structure. 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