{"paper_id":"406a41cb-eed5-4668-a896-e991042967e4","body_text":"An Explainable Machine Learning Framework for Predicting Hybrid Maize Performance Using Genomic and Phenotypic Data | 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 An Explainable Machine Learning Framework for Predicting Hybrid Maize Performance Using Genomic and Phenotypic Data DanielRaj K, RobinsonJoel M, Japhynth J, Jasperline T This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8612482/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 An application-oriented explainable machine learning framework is being used to predict hybrid maize performance based on integrated genomic and phenotypic data. Ensemble models based on tree-based learners and multilayer perceptron networks were developed to predict yield under both drought tolerance and disease resistance. To include model interpretability SHAP-based feature attributions were applied to detect the major genomic regions contributing to variation in trait attributes. Hybrid rankings on competing agronomic objectives were derived using a Pareto-based multi-trait optimization strategy to enhance decision-making for breeders. The framework performed moderately to highly in predicting different traits (R² = ~0.75) and is transparent in shedding light on how the models behave and trade-offs across traits are made. While the results are encouraging in establishing practical utility of explainable machine learning hybrid selection, larger datasets across multi-environments and field levels are needed for robust appraisal under different growing conditions. Multi-Trait Optimization Ensemble Learning Explainable Artificial Intelligence Maize Breeding Predictive Genomics Full Text Additional Declarations The authors declare that they have no competing financial or non-financial interests, affiliations, or associations that could be perceived to influence the results or discussion reported in this preprint. 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-8612482\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":575186830,\"identity\":\"ba9c067d-80e4-4057-b434-9af546ce8d2e\",\"order_by\":0,\"name\":\"DanielRaj K\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIie3PsWoCMRjA8YQUp1NXb/IVPnEufZAuka6mWLrcYOWOgC5CV4diX0GXm78QOJcrtwqF0gdwuG43dOh3Ct3OOBaaPyEhIT9CGPP5/mJYjwljXAs0ZUQnQsQXECCyaI1sL68JdxNWE9YNhtie1zsH6ezeDJbw0Rc6AAzXT7fdBZEqShtJmN9Ls4LHQaJbE3xId2plecyX+XsjARyDDUDyRIsNhmmmYiKCz8+Q4gD2G+RNoom3XzL16iR7eoWBHCX6ikg8VRsXCfcHMEuQd/QXaXsZqi0Rc+4vnWI8LKtIXm+frf0qpzO1Lqz5rKJm8tsgPi72OKP7PtU/LbOLLvt8Pt//6gfic2cmf5wiOwAAAABJRU5ErkJggg==\",\"orcid\":\"https://orcid.org/0009-0001-9863-5682\",\"institution\":\"KIT-KalaignarKarunanidhi Institute of Technology\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"DanielRaj\",\"middleName\":\"\",\"lastName\":\"K\",\"suffix\":\"\"},{\"id\":575186831,\"identity\":\"77689fa6-2314-42f1-b06d-5b7aaef6252e\",\"order_by\":1,\"name\":\"RobinsonJoel M\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"KCG College of Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"RobinsonJoel\",\"middleName\":\"\",\"lastName\":\"M\",\"suffix\":\"\"},{\"id\":575186832,\"identity\":\"af2a3084-ea81-466d-93b0-4f42622b86d0\",\"order_by\":2,\"name\":\"Japhynth J\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Dr.G.U.Pope College of Engineering\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Japhynth\",\"middleName\":\"\",\"lastName\":\"J\",\"suffix\":\"\"},{\"id\":575186833,\"identity\":\"12d27448-d74b-4677-889a-39086394cacc\",\"order_by\":3,\"name\":\"Jasperline T\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Dr.G.U.Pope College of Engineering\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jasperline\",\"middleName\":\"\",\"lastName\":\"T\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-01-15 16:40:35\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":true,\"vertebrateSubjects\":false,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":true,\"humanSubjectConsent\":true,\"humanSubjectClinicalTrial\":false,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":false},\"doi\":\"10.21203/rs.3.rs-8612482/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8612482/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":100373701,\"identity\":\"86c54e82-717b-4d96-abf8-a20289b83c74\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 08:17:36\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":857711,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"CBABFINALREVIEWPAPER.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8612482/v1_covered_8e968181-a34d-4cbe-bf2b-e1e13ea3511d.pdf\"}],\"financialInterests\":\"\\u003cp\\u003eThe authors declare that they have no competing financial or non-financial interests, affiliations, or associations that could be perceived to influence the results or discussion reported in this preprint.\\u003c/p\\u003e\",\"formattedTitle\":\"\\u003cp\\u003e\\u003cstrong\\u003eAn Explainable Machine Learning Framework for Predicting Hybrid Maize Performance Using Genomic and Phenotypic Data\\u003c/strong\\u003e\\u003c/p\\u003e\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"Dr G U Pope College of Engineering\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"Multi-Trait Optimization, Ensemble Learning, Explainable Artificial Intelligence, Maize Breeding, Predictive Genomics\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8612482/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8612482/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eAn application-oriented explainable machine learning framework is being used to predict hybrid maize performance based on integrated genomic and phenotypic data. Ensemble models based on tree-based learners and multilayer perceptron networks were developed to predict yield under both drought tolerance and disease resistance. To include model interpretability SHAP-based feature attributions were applied to detect the major genomic regions contributing to variation in trait attributes. Hybrid rankings on competing agronomic objectives were derived using a Pareto-based multi-trait optimization strategy to enhance decision-making for breeders. The framework performed moderately to highly in predicting different traits (R² = ~0.75) and is transparent in shedding light on how the models behave and trade-offs across traits are made. While the results are encouraging in establishing practical utility of explainable machine learning hybrid selection, larger datasets across multi-environments and field levels are needed for robust appraisal under different growing conditions.\\u003c/p\\u003e\",\"manuscriptTitle\":\"An Explainable Machine Learning Framework for Predicting Hybrid Maize Performance Using Genomic and Phenotypic Data\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-16 03:13:45\",\"doi\":\"10.21203/rs.3.rs-8612482/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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}}],\"origin\":\"\",\"ownerIdentity\":\"5aaae070-f14a-45b6-a24d-89498cf24ebb\",\"owner\":[],\"postedDate\":\"January 16th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-01-16T03:13:45+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-01-16 03:13:45\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8612482\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8612482\",\"identity\":\"rs-8612482\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}