Using Realistic Images for Plant Classification and Effect on Classification Difficulty

Using Realistic Images for Plant Classification and Effect on Classification Difficulty | 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 Using Realistic Images for Plant Classification and Effect on Classification Difficulty Yaqeen Salatneh Ashqer, Marwan Bikdash, Chyi Lyi (Kathleen) Liang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4486812/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 We proposed a hybrid computer-vision framework that distinguishes different plant species (such as radish and weeds) that combines machine learning methods (such as Support Vector Machine (SVM) and Random Forest (RF) classifiers) with application-specific features and image processing methods such as our own plant leaf isolation algorithm. The designed features include geometrical features that are sensitive to plant shape, as well as moment- invariant and texture features. The accuracy obtained using the combination of the designed features, and the isolation algorithm was 81.1% using SVM and 88.4% using Random Forest. We used 10-fold cross-validation to illustrate the importance of designing and selecting good features. We compared our designs with generic deep neural networks. We also compared our features with other features, such as SURF features classification, and our methods were more robust and produced better results. Throughout, we used realistic images obtained in the field, where the quality of the images depends on many factors such as lighting, seasons, occlusions, etc. We therefore include a careful discussion of the difficulty of classification problems and its dependence on the quality of images, and we propose computable definitions of problem difficulty, robustness, level of corruption, and degree of performance degradation due to corruption in the context of precision agriculture. Computer Vision Crop Classification Weed Classification Machine Learning SVM Random Forest Measures of Image corruption Classification Difficulty 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. 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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-4486812","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":316069910,"identity":"445ff768-2c15-48ad-a470-c6d1a769deb3","order_by":0,"name":"Yaqeen Salatneh Ashqer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYLCCByBCgvkAiJQhTksCgwFQMVsCSAsPKVp4DEBswloMjh9/+CGh5k8ev3TP51c3aix4GNgPH92AV8uZHGOJhGMGxZJzzm6zzjkGdBhPWtoNvFoO5DBIJLAZJG64kbvNOIcNqEWCxwy/lvPPH/9I+GeQuP9GzjPjnH/EaLmRYCaR2Aa0RSKH+XFuGxFaJG+8MbNI7DNOnHEjzYw5t0+Ch42QX/jOpz++8eGbXGL/jOTHn3O+1cnxsx8+hleLwgEEm00CTOJTDgLyDQg28wdCqkfBKBgFo2BkAgD19Uv1igJ7+AAAAABJRU5ErkJggg==","orcid":"","institution":"North Carolina Agricultural and Technical State University","correspondingAuthor":true,"prefix":"","firstName":"Yaqeen","middleName":"Salatneh","lastName":"Ashqer","suffix":""},{"id":316069911,"identity":"62b40a7d-16ca-42b5-b190-7060e73d15a7","order_by":1,"name":"Marwan Bikdash","email":"","orcid":"","institution":"North Carolina Agricultural and Technical State University","correspondingAuthor":false,"prefix":"","firstName":"Marwan","middleName":"","lastName":"Bikdash","suffix":""},{"id":316069912,"identity":"bdf56d19-a336-48a4-97aa-86fb61176a91","order_by":2,"name":"Chyi Lyi (Kathleen) Liang","email":"","orcid":"","institution":"North Carolina Agricultural and Technical State University","correspondingAuthor":false,"prefix":"","firstName":"Chyi","middleName":"Lyi (Kathleen)","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2024-05-27 19:27:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4486812/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4486812/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71484204,"identity":"313427e6-70da-4836-a268-f024c42491b1","added_by":"auto","created_at":"2024-12-16 06:25:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2224401,"visible":true,"origin":"","legend":"","description":"","filename":"ClassificationDifficulty.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4486812/v1_covered_7711ff67-e8d5-4788-845e-0ab935c673b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Using Realistic Images for Plant Classification and Effect on Classification Difficulty","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Computer Vision, Crop Classification, Weed Classification, Machine Learning, SVM, Random Forest, Measures of Image corruption, Classification Difficulty","lastPublishedDoi":"10.21203/rs.3.rs-4486812/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4486812/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe proposed a hybrid computer-vision framework that distinguishes different plant species (such as radish and weeds) that combines machine learning methods (such as Support Vector Machine (SVM) and Random Forest (RF) classifiers) with application-specific features and image processing methods such as our own plant leaf isolation algorithm. 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